{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# https://www.jianshu.com/p/3bb2cc453df1 \n",
    "# seaborn  美化matpoltlib \n",
    "# API http://seaborn.pydata.org/api.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "  species  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline \n",
    "sns.set(style=\"white\", color_codes=True)\n",
    "from sklearn.datasets import load_iris \n",
    "iris_data = load_iris() \n",
    "iris = pd.DataFrame(iris_data['data'], columns=iris_data['feature_names']) \n",
    "iris = pd.merge(iris, pd.DataFrame(iris_data['target'], columns=['species']), left_index=True, right_index=True) \n",
    "labels = dict(zip([0,1,2], iris_data['target_names'])) \n",
    "iris['species'] = iris['species'].apply(lambda x: labels[x]) \n",
    "iris.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18520728a58>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"sepal length (cm)\",y=\"sepal width (cm)\",data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a1d5d50d68>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x2Lzr7AGwsyciIlG4FfavvfYaBgwYcN3H9Ho9unfvjpkzZ2LFihWora3Fe++959ciQ4m3s/EBdvZERCQOnyfoRUVFYcmSJcjKyoJcLsf06dOxZcsWf9QWkrxdZw/Ud/Ym7qBHRERB5nPYFxUVYdmyZa6/C4IAudyzrWTDidniwzC+Ug6L1eHnioiIiJrmc9ir1WosWLAA58+fhyAIWLp0KW655RZ/1BaSfOrslezsiYgo+LwO+0ceeQSHDx9GYmIi5s2bh8ceewy33XYbBEHAtGnT/FljyBAEARYvl94BnKBHRETi8Gi8fePGja4/L1myxPXn3Nxc5Obm+q+qEGW1OSAInp9l78QJekREJAbuoOcBZ1fubdirlXJ29kREFHQMew9cOsveh2F8dvZERBRkDHsPOLty39bZ2yAIgj/LIiIiahLD3gPOrlztZWevVsrgEACbncvviIgoeBj2HnAN4yu820fAdcwth/KJiCiIGPYesFh9vGev5Jn2REQUfAx7D/g6G9/5OhM7eyIiCiKGvQecw+9KhXc/NpVSfsV1iIiIgoFh7wGztX6rW2doe8o1jM+wJyKiIGLYe+DSBD3fhvGdHxqIiIiCgWHvAbO/JuixsycioiBi2Hvg0j1779fZA5ygR0REwcWw94DZaodCLoVMKvHq9c71+Qx7IiIKJoa9B3w5yx64fJ0979kTEVHwMOw9YLZ4f5Y9cGkYn/fsiYgomBj2HjBb7V7frwcAhVwKqYRhT0REwcWw94DZ4tswvkQigUop4z17IqIw9sgjj8BkMoldhke82x2mhTJbfRvGB+o35OHe+ERE4WvJkiVil+Axhr0HfO3sgfqNdUwWTtAjIgqGCxcuYMaMGdDpdGjdujXuvvtufPnllzAYDCgvL8e0adMwZcoU6HQ6zJ49G+fPn4dGo8G8efOQlZWFTZs24e2334bdbse4cePw3//93xg1ahRWr14NQRCu+5pXX30Vv/76KxwOB1544QUMGTJE7B8Dw94TZqsd0VqFT9dQK2W8Z09EFCTLly/HpEmTcPfdd+Ojjz7CiRMncPDgQXz33XdQq9WYOHEiRo4ciY8//hhjx47FrbfeiiNHjuCll17CBx98gFdeeQVffvklkpKSMG3aNEyaNMl17Xffffea17zxxhs4evQoVq5cifz8fHz//fcM+3Bjtvg2QQ+oX37HsCciCo6BAwfiqaeewr59+3DbbbdBrVbj5MmTSElJAQAMHToU+/fvx65du7Bt2za8++67AACdTofffvsNWVlZSEtLAwB8+umnV1z7eq9JS0uDTqfDH//4R9x6662YPn16EL/bxjHsPWCx2V3L57ylVso5jE9EFCSDBg3Ct99+i59++gnvvPMOJk6cCLn8UvQJggCZTAa73Y7FixejdevWAIDS0lJUVlZeca2SkhLExsa6/n691ygUCnzzzTf45Zdf8MMPP2DZsmVYsWJFEL7TpnE2vgf8cc9eqZBxgh4RUZC8+eab2LFjB+69915MmzYNn332GXbu3Ina2lrU1tZi165d6NevH/r164evv/4aALBx40Y89dRTyMzMREFBAcrLy2Gz2fDkk0+iqKjIde3rvebYsWN44okncNNNN+Hll19GaWkp7Hbxf+ezs/dA/Wx8335kaqUMJrP4/8cTEbUEU6ZMwYwZM/DRRx8hOjoajz76KD755BP813/9F2pqavD4448jOTkZTz75JF544QWMHz8eKpUKb7zxBtRqNebOnYuHH34YdrsdEydORKdOnVzXvt5rOnXqhLZt22LcuHFQKpV49tlnIZP51iT6g0QQBEGsNy8sLMTo0aOxYcMGtG3bVqwy3CIIAu6YsQqTb+mK+27r5vV1/uc/+3HgtzJ8ODfXj9UREZE7du3ahaVLl+If//iH2KUEFYfx3WSxOQB4f7ytk0rJYXwiIgouDuO7yTmD3td79mruoEdEJJpBgwZh0KBBYpcRdOzs3eQKe187e4UMVpsDdodod0+IiKiFYdi7yXksrc876DVM8DNz+R0REQUJw95NfuvsecwtEREFGcPeTc5Jdf64Zw+A9+2JiChoGPZu8ldnr24YxucuekRE4eerr77CmjVrxC7DYwx7N/mrs+cwPhFR+Nq3bx8sFovYZXiMS+/c5L/O3jmMz86eiCgUlJSUYMaMGTAYDJBKpZgzZw6kUin++te/wmQyISEhAa+88grOnz+PjRs3YufOnUhJSUH37t0xe/ZsFBUVQS6X46mnnsLw4cOxY8cOLFiwAAAQFxeHv//970hMTMTChQuxY8cO1NTUIDU1FQsXLkRycnJQvkeGvZsudfa+bpfrHMZnZ09EBAAbfz2H9bvPBeTatwxsj1ED2jf5nGXLluGmm27Cww8/jK1bt2LPnj1YvXo1/vWvf6F169b4+eef8eKLL+Kjjz7CqFGjMHDgQAwbNgx/+ctfMHjwYEybNg3nz5/HlClT8O233+K9997Dyy+/jN69e2PJkiU4evQo2rVrh9OnT+M///kPpFIpnn32WaxatSpop+Ix7N3k79n4DHsiotAwZMgQ/PnPf8axY8cwYsQIjBgxAu+99x4ee+wx13N0Ot01r9u5cyfmz58PAGjXrh369OmDgwcPYvTo0XjiiSdw8803Y/To0bjxxhsBAM899xy+/vprnDlzBgcOHED79k1/CPEnhr2bXJ29j2GvUXGdPRHR5UYNaL77DqT+/fvju+++w+bNm/H999/j66+/Rtu2bbFy5UoA9UfZVlRUXPO6q4+WEQQBdrsdU6dOxciRI7Fp0yYsWLAAhw4dwrBhw/DMM89g6tSpyM3NhVQqveb1gcQJem5ydvZKuW8/Mi69IyIKLW+++SZWrVqFiRMnYu7cuTh+/Dhqamrw66+/AgC++eYbzJgxAwAgk8lcR9YOHjwYy5YtAwCcP38e+/btQ05ODu6++27o9XpMnToVU6dOxdGjR7Fnzx4MHDgQU6ZMQUZGBjZv3hzUo2/Z2bvJbLVDqZBBIpH4dB0Vl94REYWUBx54AM888wyWL18OmUyGBQsWIC4uDq+99hrMZjOio6Pxt7/9DQAwdOhQvP3224iJicHs2bMxd+5cLF++HAAwf/58pKam4umnn8asWbMgl8uh1Woxf/58aDQaPPHEExg/fjwAoFevXigsLAza98gjbt30/jcHse1gEZbOu93na905cxUmjeyEB8f08ENlRERETeMwvpvMVrvP9+udePIdEREFE8PeTSaL3XW/3VcqpRwmM4fxiYgoOBj2bjJb7D7vnuekVsq4gx4REQUNw95NFqvdNbnOV2qlnMP4REQUNAx7N5ksNr919iqljLPxiYgoaBj2bjJb/DtBj8P4REQULAx7N/l1Nr5Kzs6eiIiChmHvJn9P0OM9eyKiyPTFF1/giy++8Ph1y5cvx6xZswJQEXfQc5vJr8P47OyJiCLVlClTxC7hGgx7NwiCALPV7jqe1lf1w/js7ImIQoFzG9vc3FwAwKRJk/Dyyy9j4cKFqK6uhlqtxosvvogePXpg1qxZqK6uxtmzZzFz5kzs2bMHv/zyC6RSKW6++WY88cQT+Oc//wkA+POf/4zVq1fj/fffh0QiQXZ2Nl599VXYbDbMmTMHJ06cgEQiwUMPPYQ777zzipoOHDjg2q43ISEB8+bNQ4cOHfDAAw8gLi4O+fn5eOedd9C9e3e3vkeGvRtsdgEOh+D3dfYOhwCp1Le99omIwl3doc2oO7gxINeO6TMKMb1vavI5EyZMwOrVq5Gbm4uCggKYzWa8/vrrmDt3Lnr06IGTJ0/i8ccfxw8//AAAiI+Px7/+9S9cuHABf//73/Hdd9/BaDTi+eefh9lsdl23tLQUf/3rX7F8+XKkp6dj5syZ2LJlC/bt24eEhASsWbMGlZWVuPvuu9GtWzfX6ywWC55++mm888476N27N9auXYunn34a33zzDQCga9euWLRokUc/B96zd4PzOFp/DuMDl47NJSIi8YwYMQL79++HTqfDmjVrMGbMGOTl5eH555/HhAkT8Mwzz8BgMKCqqgoA0Lt3bwBAWloaVCoVJk+ejE8++QQzZsyASqVyXXf//v3o168f0tPTAQALFizAzTffjJ07d+Kuu+4CACQmJmL06NHYvXu363UFBQWIjY11vc/tt9+Oc+fOoa6u7or39wQ7eze4zrL3V2evajjm1mxznW9PRNRSxfS+qdnuO5CUSiVGjhyJjRs3Yt26dVi8eDE++OAD13n2AFBSUoL4+HgAgFqtBgDI5XJ8/fXX2L17N7Zu3YrJkyfj008/db1GLpdfcVJqZWUlAFxzjr0gCFccd+twOK6p8fLnON/fE+zs3eBcE+/vzp737YmIQsOECRPw4YcfIj4+Hm3atEFGRoYr7H/55Rfcd99917zm6NGjuP/++3HDDTfgueeeQ1ZWFs6cOeN6PDs7GwcOHEB5eTkA4PXXX8eGDRswePBgLFu2DED9B4ANGzZg4MCBrtd17NgR1dXVOHToEADg+++/R+vWrV0fNrzBttINzlD210E4zutwRj4RUWjo378/6urqXDPpFyxYgJdffhn//ve/oVAosHDhwiu6dADo0aMHcnJyMG7cOGg0GvTr1w/Dhw/HkSNHANQP88+ePRsPPfQQHA4HcnJyMGnSJBiNRrz88ssYP3487HY7Hn30UfTs2RMnTpwAUD/SsHDhQrz66qswGo2Ii4vDwoULffr+eJ69G46dqcSzi37Gy48MRv9uaT5fb9+JMrz0vzvwtyd+hx6ZSX6okIiIqHEcxneDswP329I7V2fPYXwiIgo8hr0bXBP0/DSM75yUxzPtiYgoGBj2bvD3PXsVO3siIgoihr0bzH4fxnfOxmdnT0REgcewd4P/l94519mzsyciosBj2LvB/0vv2NkTEVHwMOzdYLLYIJVKIJf558cllUqg4jG3REQha/bs2Th8+LDbz9+wYQP+53/+x6/X9CduquMGs7X+LPurN1TwhUYp52x8IqIQ9dprr3n0/NGjR2P06NF+vaY/udWq6nQ6jBs3DoWFhdc8duzYMUyaNAm5ubmYPXs2bLbICzCzxe63IXwntUoGI4fxiYhE98QTT7hOtAPqj7jt378/du3ahV27duGuu+7CpEmT8Nxzz6Gurg6PPfYYxo4di0cffRR33nknCgsLsXz5csyaNQsAMGrUKLzzzju46667MHbsWOTl5QEAHnjgAezatQuCIGDBggXIzc3FmDFj8PHHHwMAdu/ejSlTpmDixIkYPXo0fvrpJ799j8129gcPHsScOXNQUFBw3cdnzpyJ+fPnIycnBy+88AK++uor3HvvvX4rMBSYzP47y95Jzc6eiAgAsOXMTmw6sz0g1x6ZORQjMgc3+ZzrHXHbo0cP1+MFBQXYtGkTYmJi8MYbbyAzMxPvv/8+Dh8+jHvuuee614yPj8eyZcvw6aefYvHixa4z7gFg3bp12LdvH1avXg2r1Yp7770XY8aMwWeffYb58+cjKysLO3bswOuvv46bb77ZLz+HZjv7r776Ci+99BJSU1OveezChQswmUzIyckBUP9paN26dX4pLJSYrTa/zcR30qjknI1PRBQCrj7i9o477rji8czMTMTExACoPxRnwoQJAOoPuunSpct1rzls2DAAQOfOnVFdXX3FY3v27MHtt98OpVKJqKgorFy5EikpKViwYAHy8/Px7rvv4sMPP4Rer/fb99hsu9rUPYaysjKkpKS4/p6SkoLS0lL/VBZCTBa738NerZTBwM6eiAgjMgc3230H0vWOuN22bZvr8cuPlJXJZNccUXs9znPtrzfX6+qjbwsLC5GYmIgHHngAgwYNwqBBgzBkyBDMmDHDl2/rCj5NL3c4HFcULAiCXyexhYrA3LPnMD4RUai4+ojbxgwZMgSrV68GAJw4cQL5+fke594NN9yAH3/8EVarFUajEQ8//DBOnjyJgoIC/OUvf8Hw4cOxYcOGK86495VPN6LT09Nd5/QCQEVFxXWH+8Od2WJHbJTSr9fUqOQwcukdEVFIuPqI28Y8/vjjeP755zF+/Hi0b98eycnJV3T+7rjllluQl5eHSZMmweFw4MEHH0Tv3r1dE/rkcjkGDx4Mk8kEg8EArVbry7cGwIPg7ARZAAAgAElEQVQjbkeNGoVPPvnkmqNox40bh1deeQX9+/fHiy++iA4dOuDhhx92683D5YjbP/31J3RqF4+Z9w/w2zXf/+Ygth0swtJ5t/vtmkREFFgrV65E27Zt0b9/fxQVFeH+++/HTz/9BKk0tLet8aqzf+SRR/Dkk08iOzsbb731FubMmQOdToeePXviwQcf9HeNojNZ6tfZ+5OGw/hERGGnY8eOeOmll+BwOCCVSjFv3ryQD3rAg7DfuHGj689Llixx/blbt25YtmyZf6sKMWaLDWqVn5feqeSw2Byw2x2Q+WlnPiIiCqzs7GwsX75c7DI8xpRxgykQE/Rc++Pzvj0REQUWw74ZVpsDdofg9011NCrnmfYcyiciosBi2DfD5DrLPjCdvZH37YmIKMAY9s1w7nKn8ntnz7AnIqLgYNg3w9nZO4fd/UXtHMbnlrlERBRgDPtmXBrGD1Bnz3v2REQUYAz7Zjhny/t/b/yGsDcx7ImIKLAY9s1wbnyj8fM6e63aufSOYU9ERIHFsG9GwDt7TtAjIqIAY9g3wxyge/bOHfk4jE9ERIHGsG+GsWG2vL/X2cukEigVMp58R0REAcewb4ZrNr6f79kDgFYl5zA+EREFHMO+GSaLHRIJoJT7/0elVsl48h0REQUcw74ZJosNaqUcEonE79fWsLMnIqIgYNg3wxyAE++c1EqGPRERBR7DvhlGs83vM/GdNGqGPRERBR7Dvhkms93vG+o4adjZExFREDDsm2Gy2FyH1vgb79kTEVEwMOybYTDbAtfZq+WcjU9ERAHHsG+GyWwLyBp74FJnLwhCQK5PREQEMOybZTLboAnUBD2VHA6hfsY/ERFRoDDsm2G02KFRBy7sAR6GQ0REgcWwb4IgCPXD+AFaZ8+wJ08IDjscFqPYZRBRGApMyxohrDYH7A4hcBP0Gq5rYNhTIxw2C3QHN6Jm7zpYLxYBDgdUrbIQ02cUYvrdGpCdHYko8jDsm+DsuAO1qY5WzWNuqXHm4lMoXf532KpLoWrdGfGDJwBSGQwn96Fi3f/CcOYgUsc9Dqk6SuxSiSjEMeybYGqYOBfozp7D+HS1uoMbUb52MWTaOKRPeRGazD6uLj5h+D2o2b0GlRs/Rek3C5A+5UVIpIG51UREkYH37JvgXAPPYXwKpprda1C+5l1o2nVH24ffgrZjzhXD9RKJBPGDxiNlzKMwFhxG5aalIlZLROGAnX0TjK6z7APTNbmG8Rn21KBm9xpcXP8htF0HIe3OpyCRKxp9bkyfUTAV5aNm50pos/pCk5EdxEqJKJyws2+C8156wA7CUfGePV1Se2ADLq7/EFHdBiNt0jNNBr1T0i3TII9LxcX1H0FwcL8GIro+hn0TTJbADuM7P0Swsyf9b3tQ8f2/oOnYB6kT/p/b9+ClciUSR90PS1kB6g5tDmyRRBS2GPZNcIawNkCb6kilEmhUMhjM1oBcn8KD6UI+yla8DVV6JtJ+P9Otjv5yUd2HQtWmK6q2fAHBxv+WiOhaDPsmGEyB7ezrr63gMH4LZq0uRclXr0MWHY+0P7wAqVLj8TUkEgkSht8Du64KuqPbAlAlEYU7hn0TAt3ZO6/N2fgtk92kR8mXrwMOB9Inz4E8Ot7ra2kye0OR0g41u9bwYCUiugbDvgkGkw1ymQQKeeDWMPNM+5ZJsNtQtvwtWCtLkHbXTCiT2vh0PYlEgrgbxsFSVgDT2Tw/VUlEkYJh3wSj2QaNyrP7p57SquUcxm+BLq7/EMYzh5Ay5k/QdOjll2tG9xoGqTYWNb+u9cv1iChyMOybYDBZA3binZNWrYDBxElVLUnt3h9Qu3cd4gbfgZg+o/x2XalChZhew2HI3wu7oc5v1yWi8Mewb4LRbIM2gJPzgPphfN6zbzmM546i4scPoMnqi8SR9/v9+tG9RwIOG3RHf/H7tYkofDHsm2Aw2QI6Ex9omKDHYfwWwVZXhbLlf4ciPg2pdz4VkP3sVWkZUKZ2gO7wZr9fm4jCF8O+CUazLaAz8YH6YXyjycoZ1BFOcNhR9u1COCxGpN01E7IAnlQXnX0TzEX5sFQUBuw9iCi8MOybEJTOXiWHQwDMFm51Gsmqtn4F07kjSL79v6BMaR/Q94ruOQyAhEP5ROTCsG9C/Wz8wA/jAzz5LpKZCo+jevtyRPe+CTHZNwX8/eQxCVC36wbDiZ0Bfy8iCg8M+yYYzVZo1YFdeqdpuD7X2kcmh8WIslX/hDw2Ccm3PhS0943qNhiWsnOwVhYF7T2JKHQx7BvhcAgwmu3B6+y5/C4iVW35D2xVJUgZ/2dIVdqgvW9Ut8EAAP1xdvdExLBvVDC2ygXgWtpnMLKzjzTmktOo2fM9YvreAk2HnkF9b3lsMlStOkF/fFdQ35eIQhPDvhH6hk470MP4zuvz5LvIIggOVKz9X8i0MQFZT++OqG6DYS4+CVtthSjvT0Shg2HfCOfa9yhNcIbx9ezsI4ruyDaYi/KROOpByDTRotSg7TwAAGA4uU+U9yei0BHYJAtjhiB19lEaxRXvR+HPYbOgavPnUKZ3RHT2cNHqUCS3hTwuFYaTexHb79agva/D4YDBZoTBYoTeaoTOokedWYcaUx1qzLWoM+thsBphtdsglUihVaiRGp2Mjgnt0SO1C5SywP6bI2qJGPaNcHX2Ab5n75wAqOcuehGj9td1sNWUo9W4xyGRiDd4JpFIoO3UD3UHN8JhNUOqUPn1+g6HA0fLf0Ne2W84W12IEl05akx10Fn0TdYUrYxClEIDhVQOBwTozHrUmOv38lfJVRjSrh/u6jEGqdHJfq2XqCVj2DdCbwxOZy+XSaFSytjZRwiHxYTq7cuh6ZgDTUa22OVA26k/aveug+nsEWg79fPLNc02C1Yd/xE/ntyKGnMdpBIpWsekoU1MOnqmdkGsKgZahQZRCg2ilFpEK7WIVcUgVhWNaFUUpNf5AGSymnCs4iT2FB7ElrO78PPZ3RjbZRQmZ0+APADbChO1NAz7RjjD1znMHkhRarnrwwWFt9r9P8JhrEPCsD+IXQoAQN2hJyRyJQwn9/ol7PcX5+F/93yOi8Yq9G+djREZg9G3VS+o5Erf6lSo0bdVL/Rt1Qt39RyLL/NWY9Xx9ThRfgr/b+jDSNIm+Fw7UUvGsG+Ec1g90Evv6t9DwcNwIoDDakbNjpXQZGRD3bar2OUAqD/2VpORDcOpfRAEARKJxOtr/ZC/Bf+3/0u0i22NvwyZjm4pnfxY6SWJ2ng8NvAB9Envjn/t+QwvbngLr4x6GilRSQF5P6KWgLPxG2EwWSGTSqBSBH4IMUqtcC31o/ClO7QJdn014n93l9ilXEGT1Re26jLYqkq8vsaKo+vwwb7/oF+rXph/88yABf3lhrYfgFdGPQOj1YhXNi1EhaEy4O9JFKkY9o3QG+u3yvWlE3JX/TG3DPtwJggO1Oz+DqrWnaFuH9wNdJqj7dgHAGA4fdCr1+8q3I8vDq/E79rfgJk3Pgq13L8T/ZqSmdAOs0c8iTqLHn/dsggmmzlo700USRj2jTCYbAFfY++k1Si4zj7MGU/th7WyCHEDxwXlA6In5AmtII9PhfH0AY9fe7a6EIt2foTOSZl4dOADkEqD/yujU1IGnhn6XyisK8HiPZ/xOGgiLzDsG6E3Bf4QHKcotYKdfZir2f0dZDGJrj3pQ4lEIoEmMwfGs4ch2N3/UGl32LFo18fQKNSYceOfRF3/3ju9Oyb3ugO/nPsV6/I3i1YHUbhi2DdCZ7AiOggz8YH6YXyusw9flopCGM8cRGz/2yGRheacV23HHAgWE0wXTrj9mu9+24iz1YV4qP9kJGjiAlide+7snot+rbPx2aEVKKorFbscorDCsG+EzmgNyrI7oH55n8Vqh9XmCMr7kX/V7l8PSOWIzRktdimN0mT0AiRSGN28b1+mv4iv89ZgQOveGNgmJ8DVuUcikeBPA+6DUqbA+7s/hcPBfy9E7mLYN0JvtASts4/mlrlhy2E1Q3doM6K6DYIsSvzutzFSdRRUrTvDeOaQW8///NC3AIDp/e4JqTkICZo4TOv7B5yoOIV1JzeLXQ5R2GDYN0JnsCJa69tGIe5yjiDouLFO2NEf3wmHSYfYvreIXUqzNJnZMBefgsPU+Ha2AHC+pgg7zu3F7V1GIjkqMUjVuW9Yh4HISe+BLw+vRrWxRuxyiMICw/46LFY7LDZH0Dp7Z9hzF73wU7d/PeQJ6VAH+bx6b2gyewOCA8azR5p83tdHvoNarsL4rjcHqTLPSCQSTO33B1gcVnx+eKXY5RCFBYb9dTg77GhtcIfx2dmHF2tlMUznjyE2Z7SoB964S92mCyQKFYwFjQ/ln60uxM7z+3B7l5GIUYlzNK87WsekYWyXUdh8ZgdOXiwQuxyikBf6v6FEoDNYAAAxmuAO4+sNDPtwUndoMyCRIrrXCLFLcYtEpoC6XY8m79uvPL4eGrka47qG7mRDp0k9bke8OhYf7/+aa++JmsGwvw5nhx0V7M6eE/TChiA4UHd4MzSZfSCPDZ892zWZvWG9eAG22ovXPFZlrMGO83sxMnMIopVRIlTnGa1Cg7t6jsWJi6exrzhP7HKIQhrD/jpcw/i8Z0+NMBXkwV5bgZg+I8UuxSOazN4AAGPB4WseW3/qZzgcDtzW+aYgV+W9UR1vRHp0Cj4/9C2X4hE1wa2wX716NcaMGYNbb70VS5cuvebxRYsWYeTIkZgwYQImTJhw3eeEE+cwfrDu2asUMshlEoZ9GKk7tAlSlRbaLjeIXYpHlKntIdXGXhP2VrsV60/9jL6teiI9JlWk6jwnl8owOfsOnK8pwrZze8QuhyhkNbvdV2lpKRYuXIjly5dDqVRi8uTJGDRoEDp1unTqVV5eHt5++2307ds3oMUGi87g7OyDc89eIpEgWqPkBL0w4bAYoT+xC9G9hkPq4znuwSaRSKHp0BPGgsNXHHm74/w+1JhqcXuX8BqpAIDB7foh89iP+PrId7ix/QDIpIE/qZIo3DTb2W/fvh2DBw9GfHw8tFotcnNzsW7duiuek5eXh8WLF2P8+PGYN28ezObwPpmqVm+BVBK8YXwAiNLIXSMKFNr0v+2BYDUjutdwsUvxiqZDNux1F2GtLHZ9bePpX5AenYLstG4iVuYdqUSKu3uNRamuHD+f3S12OUQhqdmwLysrQ0pKiuvvqampKC29tC+1Xq9H9+7dMXPmTKxYsQK1tbV47733AlNtkNQaLIjWKiGVBm/nsGgtO/twoTu8BfK4VKjbhV8wAvWb6wCAqWEJXomuHEfL83FT5hBIw2AJ4fX0b90bmfHt8M3RtbA77GKXQxRymv2X7XA4rtgu8/KhPwCIiorCkiVLkJWVBblcjunTp2PLli2BqTZIavUWxEYFd3g2RqtkZx8GbLoqGM8cQnSvYWGxtv565AmtIItNhrGgfgb75jM7IJFIMCIj9E7sc5dEImF3T9SEZn9bpaeno7y83PX38vJypKZemsBTVFSEZcuWuf4uCALk8tA8+ctddXoLYoK0Va5TtFaBOq6zD3n6o78AgiNsh/CBhiNvM3rBePYw7HYbthTsRJ+07kjSJohdmk+c3f2Ko+s4M5/oKs2G/dChQ7Fjxw5UVlbCaDTixx9/xPDhl37RqdVqLFiwAOfPn4cgCFi6dCluuSX09wlvihidfaxWiTp29iGv7vBWKNOzoExuK3YpPtFkZMNh1GH/iS24aKjCTZlDxS7JZxKJBBN73IZiXRl2FO4VuxyikNJs2KelpeGpp57Cgw8+iDvvvBPjxo1D79698cgjj+Dw4cNITEzEvHnz8Nhjj+G2226DIAiYNm1aMGoPGDHCPlqrhMFkg83OjiRUWS5egKXkFKJ7DRO7FJ9pOtTft996ejuiFBoMaNNb5Ir8Y2DbHLSJSceKoz/AIfDfEpGTW+Pt48ePx/jx46/42pIlS1x/zs3NRW5urn8rE4kgCCJ19pc21omLVgX1vck9uryt9dvj9vid2KX4TB6bBCGpFfbrinFj1lAoZcFbeRJIUokUd3bPxbu7P8a+osMY0KaP2CURhYTwnGEUQCaLHTa7Q5TOHqgfVaDQIwgCdHk/Q5ORDXlMeN/bdjrZuj3MEgE3to2M/TGcbuxwA1KikrDi6DrumU/UgGF/lRpd/R4BYszGBy5t6EOhxXzhBGzVpRExhO+0X2FDjM2OLGvwlpgGg1wqwx1db0F+ZQGOlueLXQ5RSGDYX6W6IezjY9RBfd+YqPphVE7SC011h7dAIlciqmv4Lk+7nN5iwCFdEfrozDCfjbxDZEZmDkGcKgbfHvtB7FKIQgLD/irVdQ1hH+T75jEcxg9Zgs0K/dHtiOo6CFKVRuxy/GJX4QHYHHbcoExu8sjbcKWUKzGmyygcLDmK05XnxC6HSHQM+6u4wj4muGHvvG3AsA89hpP74DDpEJ0dHufWu2PH+b1Ii0pG5/Z9YLqQD4fFKHZJfpfbaQQ0CjW+Pc7unohhf5WqhrAP9ox4jUoOhVzqmjNAoaMubwtkUfGu42HDnc6sR17pcQxu1w/ajn0Ahw2mc8fELsvvtEoNbs0ajl2F+1FcVyZ2OUSiYthfpbrOhGiNAgp5cH80EokEcdEq1OgZ9qHErq+BIf/X+u1xI+Q0td0XDsIuODCkXX+o23aDRKaAsSDyhvIBYGyXUZBLZFh1fL3YpRCJimF/lWqdOehD+E5x0UrU6DiMH0p0R34GHHbE9B4ldil+4xzCz0xoB6lCBXW7bhF53x4A4jVxGJk5FFsKdqLSWC12OUSiYdhfpbpOxLCPUnEYP8TUHdwEVassKFPbi12KX1w+hO880EqT2RuWsrOw6SIzDMd3uxkOwYE1JzaIXQqRaBj2V6mqNSMhyMvunOo7e4Z9qDCXnIalrADREdTVXxrC7+f6miajfi5CpA7lp0WnYGj7AVh/6mfUmXVil0MkCob9ZQRBQEWNEUlxYoW9CjWcjR8y6g5sgESmQHTP8N8e12lX4X6kRiUhM+HSSIUyPRNSTTSMZw6KWFlgTeyeC7PNjLX5m8QuhUgUDPvL1OotsNocSI4XZy11XLQKZosdRrNNlPenSxwWI+oOb0FUj6GQaaLFLscv9BYDDpUew6C2fV1D+AAgkcqgyegN4+mDEbu9bLu41rihTR+s/W0TjFaT2OUQBR3D/jKVtfW/BJLjxAn7hIa5As61/iQe3ZFfIFiMiO17q9il+M3eosOwO+wYfNkQvpOmYw7suipYy8+LUFlwTOx+G/RWI344uUXsUoiCjmF/mYrq+o1FxBrGT4ytf1/nhw4ST+2+H6FIaQ9V265il+I3O8/vQ5I2AZ0SM655TNux/nQ4w+kDQa4qeDolZaBPeg+sOfETTDZ+oKaWhWF/mYs19SGbJFJnn9jwIaOyhmEvJtOFfFhKTiG27y1XDHeHM6PVhIMlRzGoTc51vyd5bDIUyW1hPBO5YQ8Ad/Ucg1qzDutP/ix2KURBxbC/TEWNERIJkBArztK7pIbO/mJt5G1dGk5qdq+GRKVFTO+bxC7Fb/YVH4bVYbvuEL6TpmMOTOeOwWGN3K63a3IWstO6YdWJ9TDbOBmWWg6G/WVKKw1IjtdALhPnxxKlUUApl7pGGCj4bLUV0B/bgdic0ZCqtGKX4zc7zu9DvDoWXZI6NvocbcccCDYLTOeOBrGy4Lu751jUmGqx/hS7e2o5GPaXKb1oQFqieL/gJRIJEuPUvGcvopo93wMAYm8YI3Il/mOymrC/+AgGte0LqbTxf/Lq9j0gkSthOLkviNUFX7eUTshO64pvj62DiTPzqYVg2F+mtFIvatgD9ZP02NmLw27So27/ekR1GwRFXKrY5fjNvuIjsNqtV2ykcz1ShQrqDr1gOBXZYQ8Ak7MnoNasw/dcd08tBMO+gdlqR2WtGelJUaLWkZqgRXmVQdQaWqraPd/BYTYgfujvxS7Fr3YW7kOcOhbdkjs1+1xtp36wVZXAWlkUhMrE0zkpE/1bZ2P18fXQW/jvjSIfw75BWWX9P3ixO/u0JC0qqo2w2R2i1tHSOMwG1OxeA22XG6BKzxS7HL8x2yzYX5SHQW1ymhzCd9Jm9QWAiB/KB4B7eo2H3mrEyuM/il0KUcAx7BsUldfvmd0qWdzOPj1RC4cAlFdxRn4w1ez5Hg6THgm/+4PYpfjV/uI8mO2WJmfhX06RkA5FUusWMZSfkdAOv+swEN/9thEV+kqxyyEKKIZ9g8Ky+rBvmxojah1pDbcRSi7qRa2jJbHra1C949v6rr5V47PVw9H2c3sRp4pB95Tmh/CdtJ36w3j2CBzmyP/AOSX7DkAQ8J/Dq8QuhSigGPYNCst0SIhRIVqjELWO9MSGsK/kfcRgqdz6Hwg2CxJHPSh2KX5ltJqwt/gwBrfrB5lU5vbrtJ1vAOw2GCJ8gx0ASIlKwpguo7D17C6crjwrdjlEAcOwb1BYVod2aeJ29UD9Vr1KhQwXyngUZzBYys6hbv9PiO2XC2VSa7HL8au9RYdgtVtxY/sBHr1O3a4bpOpoGH77NUCVhZaJ3W9DrCoaH+77Cg6Bc2UoMjHsUX+07fnSOrRJFf90M6lUgg7pMSgorhG7lIgnCA6Ur/0XpGotEobdLXY5fvfLuV+RpElAl2TPbk1IpDJoO/WD4eReCA57gKoLHVqlBvf3mYQTF09jy5mdYpdDFBAMe9TvnKc32ZDVJk7sUgAAma3jcKaoNmKPGw0VtXt/hLnwBJJungaZNlbscvxKZ9HjQMlRDGnfH1KJ5//MtV1ugMNYB1PhiQBUF3qGZwxC16SO+OzQCugsnC9DkYdhD+BUYX0X3TFEwj6jVSxq9RbupBdA1uoyVG76DJrMPojOHiF2OX63u/AA7A47hrbr79XrtR1zAKkcht/2+Lmy0CSVSPFQ/ynQWfT49MByscsh8juGPYBTF6obhs9Do7vr0j4eAHD0NJcDBYJgt6Hs24WARILkMY9GzMl2l9tasAutolORldjBq9dLVVpoMntDf3xnixlhykhoiwndbsWmM9uxvzhP7HKI/IphD+DE2SpktIqFUuH+jOVA6tQ2HlFqOQ7kl4tdSkSq2volzBd+Q8qYR6GIj5xtcZ3K9RdxtDwfwzMG+fRBJqrbYNhqymApOe3H6kLb3T3Hol1sK/xrz2cczqeI0uLD3m534LdzVeiRkSh2KS4ymRS9O6fg12Ml3EnPz3THd6B6+3LE5NyM6B43il1OQPx8djcAYFjGIJ+uE9VlICCRQn+85UxaU8gU+O9Bf0SNqQ7/u+fzFjOqQZGvxYf9maJamCx29MhMEruUK4we0A6VtWbsOFQsdikRw1xagPJV/4SqTRck5z4sdjkBIQgCthbsQveUTkiN8u2/aZk2BpqMXtAd296iQi8rsQOmZE/AzsJ9WJe/WexyiPyixYf9wYah8h4dQ6ezB4ABPdLRLi0G/16Vh4s1kb+TWaBZa8pQ8p/XIFVHIe33z0IiF3fzpEA5VXkWRXWlGN7Bt67eKarbENiqSmApa1kbzozvdjP6tc7GJwe/wW8VLec2BkWuFh/2+06UIaNVLJLiNGKXcgWZVIIZ9/WH0WzFc4u2obiC9w+9ZTfUouSL+RCsJrSaPAfymASxSwqYjWe2QylTuL0XfnOiug6qH8o/us0v1wsXUokUTwz8I5K1iXhz2/so0XH+DIW3Fh32BpMVR89Uom/X0Jyk1bFNHF577EYYTDa88u8dMJptYpcUduwmPYo/nwdbTTnS/jALylTvZqeHA5PVhF/O7sHgdv0QpfTP6Y2yqDhoOvaB7sg2CC1sd7loVRReGP4EBEHA61v+iVpTndglEXmtRYf93mNlsNkdGNQzXexSGtW5XQKe/+MNuFCuxzcb88UuJ6w4THqUfPEqLOXnkXbXs9C07yl2SQG1/fxeGG0m3NxxmF+vG91zGGw15TC3kA12LtcqJhXPDftvXDRW45VNC1Fl5M6WFJ5adNjvyCtGXLQS3UJoJv71ZHdKxo29W2PVz6dhMFnFLicsOEx6FH/xKswlp5E26WnXOe2RbMOpbWgTm46uHm6P25yorgMhkSuhy/vZr9cNF12SO+L5YY+jzFCJlze+jTL9RbFLIvJYiw17k9mG3UdLMDS7NWTS0N9U5Y7hHWE027DjMGfnN8du1KH483kwl5xB2qQZ9fedI9zZ6kLkVxZgdMff+X2TIKlSA22XG+pn5dtb5ofNXmldMWfEn1FjrsPz699AXmnLG+Wg8NZiw37XkRKYLXYM69tG7FLc0j0jEelJWmzZVyh2KSHNbqhD8dKXYS4rQNrvZyCq60CxSwqKdflboJApMMLHtfWNicm+CQ5jHfQt5CS86+manIXXb3kOsapozN/yDyw78h1sLeCgIIoMLTbsN+8rRHKcGj1DbH19YyQSCQb3aoXDpy5yol4j7PoaFC+dC2tFIdLvnoWoLjeIXVJQ1Jp12Hp2F0Z0GIQYVWBObtR07ANZTBLqDmwIyPXDReuYNLx287MY2q4/vspbgxfWv8GleRQWWmTY1+jM2HeiDCP6tYU0DIbwnQZ0T4PN7sAhbqN7DVtdFYo+mwtrZQnS73mhRdyjd/rp1M+w2q0Y02VUwN5DIpUhps9IGE8fgK22ImDvEw60Cg2eHDIdM278E2pMdZizYQHe3r4EhbW8xUahq0WG/c8HLsDhEDCyfzuxS/FIj8wkKBUyHDzZsn/ZXs1WexHFn70IW20F0qfMgSazt9glBY3NbsMP+VvQJ7072sa1Cuh7xfQZBUBA3cGNAX2fcDGwbQ7+Z8zLuLvnWOwvPoJn1r6Kt35ZjN8qTreoHQcpPMjFLkAMm/cWIqNVLDq0Co1T7tylkEvRPSMBeacY9k7WmjIUf/YS7EYdWk15Eeq23cQuKai2nduDKlMNHu1yf8DfSxGfBk1mb9Tu/wnxN/4eEmloHBwlJrVCjbt7jUNu51rgFPAAABmjSURBVJvw/W8b8UP+ZuwuPICsxA4Y03kUhrTrB7msRf6apRDT4jr7kot6nDhXhZv6tRW7FK/0ykpGQXEtdAaL2KWIzlpVguJP58Jh0qPVlLktLujtDjuWH12LjPi2yEkPzh4CsQPGwF53EfoTu4PyfuEiVhWNydl34P3xr+OhfpNhtJrwz10f4rE1s/HNke9RZ9aJXSK1cC0u7H8+cAEAMCwnPGbhX61nZhIEATh+tkrsUkRlrSxC0adz4TAb0erel6Bu01nskoLul3O/okRXjrt6jvX7crvGaDv1gzw+FbV7vgvK+4UbtUKN3M4j8PbtczF7xJ/RMaEdvsxbjcdWv4BP9i9DNTflIZG0uPGlbQeK0LVDAlIT/bOdaLB1bhcPqVSC4wWVGNA9TexyRGGpKETx0lcgOGxodf8rUKVliF1S0DkcDnxz9Ht0iGuDAW2CN0dBIpUhdsDtqPzpY5hLTkOV7t8NfCKFVCJFn/Qe6JPeA+drirDy+I/4Pn8Tfjy1FeO6jsaEbrnQKNRil0ktSIvq7IsqdDhdVIPf9WktdileU6vkyGwdi2MFlWKXIgpL2VkUfzYXEOxofV/LDHoA2Hp2F4rryvD7nmMglQT3n3FMn9GQKNWo2bkqqO8brtrFtcYTg6bindtfwsA2OVh+dB2e/P4lbDu7mxP5KGhaVNjvbNh9bmh2+IY9AHTrkIj881WwO1rWLwpz0UkUfTYXkMjQ6v55UKa2F7skUZhsZnxxeCU6JWZgYNucoL+/TB2F2H650B39BdZKLjdzV3pMKp4cMh2v3/wcUrSJ+MfOD/H61kWoMLTMD+4UXC0q7LcfLkZW27iwHcJ36tohAUazHedLW84pXMZzR1G09GVIlRq0fvBVKJPDc4KlP6w6/iOqjDX4Y9+7gt7VO8UNHA+JVIbqHd+K8v7hrFNSBuaPnonp/e7B8YpTmLFuPrad5YRHCqwWE/aVtSacOFuFIb0CuxY5GLp2qD+P/cTZltER6PN/RckXr0Iek4DWD74GRULonlIYaBX6Sqw6vh5D2w9A1+Qs0eqQxyQgJmc06g5thrWmTLQ6wpVUKsVtnW/CgtzZaBvbCv/Y+SHe2/0JzDausqHAaDFhv/tICQBgUASEfaukKMRGKVvEffu6gxtR+vXfoExph9YPzIc8Njy2Nw4EQRCwZO/nkECC+3rfKXY5iB86ERKJBFVbvhS7lLCVHp2CV0Y9jUk9bseWMzsx+6c3UVLHD0/kfy0m7HfmFSMtUYsO6TFil+IziUSCbh0ScbwgcpffCYKAqp+/Qvmad6HJyEar+16BLCpO7LJEtaVgJ/YXH8G9ve9ESpT4H3rkscmIHTgWusNbYC4tELucsCWTyjA5+w48P/xxVBqr8fz6N7C/OE/ssijCtIiwN5isOJhfgcG9WgVtPXKgdctIwIVyHWr1kTfsJ9isKF/1D1Rt/RLR2Tch/Z4XIFVpxC5LVJWGany8/2t0S85CbucRYpfjEj9kIqTqKFRu+IQzy32U06on3rhlFpKjkvDG1vfw7bEf+DMlv2kRYb//RDlsdgcG9Yqce73dMxIBAMcjbCjfVleFoqUvQZe3FQkjpiBl/BOQtPDtRm0OOxbu+DdsggOPDnxAtEl51yPTRCNh+B9gPHMQ+mPbxS4n7KVGJ2P+6JkY3K4fPj/0LRbt+ggWu1XssigChM5vjQDafrgIsVFK9AiT42zd0aV9AuQyKfJOXxS7FL8xFR7Hhf+bCUtpAVInzUDC7+6KmJEYXyw9uAInKk7h0RvuQ+uY0NtIKbb/bVCmZ+Hij/8Hu0kvdjlhTyVX4v8NeQj39BqPn8/uxssb30YVd94jH0V82Fttdvx6rBSDeqZDFkbH2TZHqZCha4fIOBRHEARU71yFok/nQiJXos3UvyK6+xCxywoJm05vx3e/bcDtnUfixvY3iF3OdUmkMqSM+RPshlpcXP+R2OVEBIlEgt/3HIMZN/4J52uLMWv9X3HyYoHYZVEYi/iw33+iHAaTDTeG8a55jemV9f/bu/O4qsp9j+Mf2AMbBVRkFFQcQ5EMx1DCwHKKqzi9Qn0pZjZpWZ7OVXMsNVOcpS7pzeO5Hi2HY+Y5Hr1OHE0BEbSkAdQDKAJbRRBk3ONz//BGWZ0YxJZsnvdfstbea33ZCL+1nrXW82tNZm4xZZWNd5jPXFbMjV3vU3Tif2jWpQ++L65G69Fe6ViPhJS8i3ycuoOeXt2Y3HOM0nF+k4N3J1oGR1KWFk/Z9wlKx7EZ/XyfYFn4H1Hbq1kSv5aT2UlKR5IaKZsv9qe/zsPJUUPPLu5KR2lwQV09sAq4eKVA6Sj1Up5xltz/nk1Vzne0HvoSnmP/E3tdc6VjPRK+0n/LhsRP6NSqPW8PeLlRtEltFfo8Dj5dKTj0sZxZrwH5tfLlg2fn8ZhbJ/7r3Ha2pOyU1/GlOrPpYl9RZSLpWz0hT/igVtnet/pY+1Y006m5kNG4nss1lxVzc/86bu5bjdrFDZ9pq2jRZ5i8Pv//Tl89R8zpOHxdvJkXOhNdI2mYYqdS4xE5Gzt7e27sXoGlsunM8PiwuTg4sWDQG0R2G8rxrDMsOr6a/Ls3lI4lNSK2VwF/4szFfAxGC+G92yod5aFQq+zp7e/Jue9uNIp58oXVwt0LR8nd/Cbll5JpFRqFz9QP0Lo3zTnuf85qtbLrm78Rm7wNf/fOLAmfjYuDk9Kx6kTT0gOv8fMwldzi5l9jsBqrlI5kM1T2KiY+Hsncp2Zwu6KIOUdXcPRfX8rH86RasdliL4TgUGI27b2c8fdrpXSchyY40JviMgPfZT3aN+pV5nxH3rZ3uH14M1qPdvhOX0urp8Y3+cfqfnC7oojlpzbx+feHCeswgHdCX6eZpnHOLaBr2w2PkbOoup7BjV3LsRoqlI5kU3q3CWT1sIV0c+/MJ+c/Y+nJDejlrHtSDWz2L+03mbfJzC1hxtjHbXp4uG83T5rp1Bw7l8PjnR+9+xIMN7K58+UuKq6konJ2xWPUWzQPCLHpn0ldmK0Wjv7rFLu++RtCCF7rO5mwjgOUjvXAnLoPBODWgY3kb1+E5/g5aFo+eo8NNlauji15J/R14rMS2XHxc97+32U81zWc0d2G0UzbOA8SpYfLJou9EIJPj1yilbMDg/va9hCxzkHN0718OXYuhxciAnB1Uf76rhACQ+4lipO+oOJKCvYOzWj19ERa9IvAXuOgdLxHgsVqIen6efZ8e5AbZQU84dWd6b0n4OHkpnS0BuPUfSD2Ds249cV68rbOwW3EKzT3D5YHeg3E3s6eZzqF0LtNIJ+mfcGBjKPEZyfyXNdwhnV+WhZ96T52QsELPrm5uQwePJgTJ07g69twLUvjU6+z/rMLzBjXk+HBfg223UdV/u0yXlsVz/BgP14d87hiOSyVpZSnJ3H3wlGMN7Oxd3SmRZ8RuPR7DpW8yx64N1x/+uo5jmZ+SWHFHdq38OH5wJH0bhNos0XQdOcGNz9fh/FGJo6deuEaNgkHTz+lY9mcrKJr7P72IF/pv8VRoyO0fX8GdwyhfUsfm/2/JdVerYr93//+d+Li4jCbzURHRzNp0qT71qenp7NgwQLKy8vp06cP7733Hmp1zYMGD6PYZ+WVMO+jM/h5u/DBzBCbmkjnt8Ttu8jhpKsse2XA7/qYoan4FpXZF6m4nEJF1kWwmtF6tMel11CcAgdhr1V+pEFJFquFa8W5XLyRzlf6b8m4nQlAD4/HGNblafr4PP5ITX/7sAirhZKUQ9w5vQdhqKBZ59449xxMs869sFNrlI5nU7KKcjh0OZ6k6+cxWc34uHjR3zeInl7d6OLaoVE8xik1vBqL/c2bN5kwYQKff/45Wq2WqKgo1q1bR+fOnatfExERwfLly3niiSeYP38+PXr0YOLEiTXuvCGLvcVi5dRXuWzZ/w2OOg0xrz+Fe6umM4xVUWXij5u+5HZxFa+P70lITx/sG/BAx2qswlxSgKlIj6kwF8ONbAz5VzCX3HvGX93Cg+aP9cOpRyhar45N7kyi0lTFnaoSCsoLuVlWwPUSPTkleWQV5WCw3GtW5NfSlyfb9mJA2954OXsonFgZlsoy7qYc4u6FI1jKi7HT6HD064HO1x+tV0e0bj6onF2xawIHQA9bqaGMxJzznM29wPcFVxBCoFVp6NCqHR1btcPXxRtvZw/cm7vi6tgSjUoedNmyGov9/v37SUlJYcWKFQB89NFHCCF4/fXXAcjLyyM6Oprjx48DkJqayqZNm9i+fXuNO7927RpDhgxh586deHnVr0lNSbmBnYczuJRzh/JKEx3btODlMY/j1qLpnVEWl1YRu/ci1/R38XRtzoIX+tFMV/NRfMW/LmDIu4ywmhFmE1azEWEyIAyVWA0VmCtLwVh533tUzq3RerbHwbsLOt+uqFvZTkfBHyRf/4qcu3lYrFbMVgsWqxmT1YzRYsZgMVBlMlBuqqDCWPGLSU4c1A60cfakXUsfOrRsS9fWHXDRNf72yg1FCCuGnHQqs9OozE3HUvKTiaHsVKicWqBydMbOoRn2Wh12agfs1BqcAp5C626bj9I+TBWmSq4UZnOl8Co5JXnk3tVjNN/fMdNRo8NJ2xxHjQ5HtQNalRaNSovGXoXaXo3K3h5/98487tntgfN4eXnVavRXajg1ftq3bt3C3f3HYWEPDw/S0tL+7Xp3d3du3rxZq50XFNz7Bf/5ZYEHkQ2c2NFgm2u0soH/+OvD3oucuvO3fK10AJu0VekAUgNo6Pu0pJrVWOytVut9Z2xCiPu+rmn9b+nRowc7d+7E3d0dlUpVl9ySJElSI1XfkVyp/mos9l5eXqSmplZ/XVBQgIeHx33rfzhDB7h9+/Z963+LTqejT58+dckrSZIkSVId1XgXzIABA0hKSqKoqIjKykqOHj1KaGho9XofHx8cHBw4f/48AAcOHLhvvSRJkiRJyqr1o3ebN2/GZDIxbtw4XnrpJV566SVmzZpFYGAgGRkZLFy4kLKyMgICAvjggw/QarW/R35JkiRJkmqg6KQ6kiRJkiQ9fPJhVkmSJEmycbLYS5IkSZKNk8VekiRJkmycLPaSJEmSZOOa3HyFZWVlREVF8fHHHzfqGZw+/PBDDh8+DMCgQYOYM2eOwonqZ+PGjRw5cgQ7OzvGjRvHCy+8oHSkB7Jq1Sru3LnDypUrlY5Sb5MnT6aoqKh6OtOlS5fSs2dPhVPVXXx8PB9++CGVlZUMHDiQhQsXKh2pXvbu3cuOHT9OC5qbm8uoUaNYvHixgqnq58CBA2zZsgWA0NBQ5s6dq3CiJkQ0IV9//bWIiIgQAQEB4vr160rHqbeEhATx/PPPC4PBIIxGo5gyZYo4evSo0rHqLDk5WURFRQmTySQqKytFWFiYyMzMVDpWvSUmJor+/fuLuXPnKh2l3qxWqwgJCREmk0npKA8kJydHhISECL1eL4xGo5gwYYI4efKk0rEe2OXLl8Wzzz4rCgsLlY5SZxUVFaJv376isLBQmEwmMW7cOJGQkKB0rCajSQ3j79mzhyVLltR6hr9Hlbu7O/PmzUOr1aLRaOjUqRP5+flKx6qzfv36sX37dtRqNYWFhVgsFpo1a6Z0rHopLi5m/fr1vPrqq0pHeSBZWVkATJs2jZEjR953RtmYHDt2jBEjRuDl5YVGo2H9+vWNcnTi5959911mz56Nq6ur0lHqzGKxYLVaqaysxGw2YzabcXBwUDpWk9GkhvHff/99pSM0iC5dulT/++rVqxw+fJjPPvtMwUT1p9Fo2LRpE3/6058YNmwYnp6eSkeql8WLFzN79mz0er3SUR7I3bt3CQ4OZtGiRZhMJqZMmUKHDh0YOHCg0tHq5Nq1a2g0Gl599VX0ej1PP/00b731ltKxHkhiYiJVVVUMHz5c6Sj14uTkxJtvvsnw4cNxdHSkb9++9OrVS+lYTUaTOrO3NVeuXGHatGnMmTMHPz8/pePU26xZs0hKSkKv17Nnzx6l49TZ3r178fb2Jjg4WOkoDywoKIiYmBicnZ1xdXVl3LhxnDp1SulYdWaxWEhKSmLFihXs3r2btLQ09u/fr3SsB7Jr165GfU9LRkYG+/bt45///CenT5/G3t6erVtlF8Pfiyz2jdT58+eZOnUqb7/9NqNHj1Y6Tr1kZmaSnp4OgKOjI0OGDOHSpUsKp6q7Q4cOkZCQwKhRo9i0aRPx8fGsWLFC6Vj1kpqaSlLSj62LhRCNsu+4m5sbwcHBuLq6otPpeOaZZ+5rzd3YGI1GUlJSCA8PVzpKvZ05c4bg4GBat26NVqtlzJgxnDt3TulYTYYs9o2QXq9n5syZrFmzhueee07pOPWWm5vLwoULMRqNGI1GTpw4Qe/evZWOVWfbtm3j4MGDHDhwgFmzZhEeHs78+fOVjlUvpaWlxMTEYDAYKCsrY//+/Tz77LNKx6qzsLAwzpw5w927d7FYLJw+fZqAgAClY9XbpUuX8PPza7T3tAD4+/uTmJhIRUUFQgji4+MJDAxUOlaT0fgO2SW2bt2KwWC47/GuqKgoJkyYoGCquhs0aBBpaWlERkaiUqkYMmRIoz54sQVhYWFcvHiRyMhIrFYrEydOJCgoSOlYddazZ0+mT5/OxIkTMZlMDBw4kLFjxyodq96uX7/e6HvAh4SE8P333zNmzBg0Gg2BgYG8/PLLSsdqMmQjHEmSJEmycXIYX5IkSZJsnCz2kiRJkmTjZLGXJEmSJBsni70kSZIk2ThZ7CVJkiTJxsliLzVpe/fuZefOnTW+Ljw8nG+++abWyxvCtGnTKCoqqvN+0tPTeeeddxokw8qVK0lOTm6QbUmSpBxZ7KUm7fz581RVVSkd41clJCTU+T1Wq5UFCxY02DzwM2fOZPny5Y/sZyRJUu3ISXUkm5CcnMyaNWto06YNWVlZ6HQ6Vq5cSadOnTAajaxZs4aUlBQsFgvdu3dn4cKFJCUlER8fT0JCAjqdjqFDh7J48WIKCwspKCjAx8eHDRs20Lp161pliI+PJy4uDpPJhE6nY+7cuQQFBREbG0teXh4FBQXk5eXh6enJ6tWr8fDwIC0tjXfffReTyUS7du3Iz89n3rx5fPHFFwBER0dX9//evXs3S5YsoaioiFGjRjF79uxfZDh8+DC+vr7VDYWys7NZvHgxRUVF2Nvb89prrzFixAjCw8OJiIjg7NmzlJSUMH36dC5cuMB3332HWq0mLi4OT09PnJ2dCQoKYvfu3URHRzfQT0uSpN+dsh12JalhnD17Vvj7+4uUlBQhhBCffvqpGD16tBBCiNjYWLFy5UphtVqFEEKsXbtWLFmyRAghxNy5c8Unn3wihBDiz3/+s9i8ebMQ4l5f9+nTp4utW7cKIYQICwsTaWlpv9jvD8uzs7NFRESEKCoqEkLc6zs+cOBAUV5eLjZt2iQGDx4sSktLhRBCvPLKK2Ljxo3CZDKJ0NDQ6j7rSUlJ4rHHHhNnz54VQgjRtWvX6r7lYWFhYunSpUIIIW7duiV69Ogh8vPzf5HnjTfeEPv27av+OjIyUuzYsUMIIUR+fn51jrCwMLFixQohhBD/+Mc/hL+/v0hPTxdCCDFjxgwRFxdXvY0jR46ISZMm1ernIEnSo0me2Us2w9/fnz59+gAwduxYli5dyp07dzh58iSlpaUkJiYCYDKZfvVsPTo6mtTUVLZt28bVq1e5cuVKrXugJyQkcOvWLaZOnVq9zM7OjpycHAD69euHk5MTAN27d6ekpITLly8D96YNBnjyySfva1/8cxEREQC4u7vj5uZGYWEh3t7e970mKyuLKVOmAFBcXExGRgbjx48HwNvbm+PHj1e/dsiQIQC0bdsWNzc3/P39AWjXrh0lJSXVr/P19SU7O7tWn4MkSY8mWewlm6FSqX51mdVqZf78+dVFtby8HIPB8IvXrl69mrS0NMaOHUv//v0xm82IWs4mbbVaCQ4OZsOGDdXL9Ho9Hh4eHDt2DJ1OV73czs4OIQQqleoX2/+17+EHP+0+98M2fu6ny394vZ2dXfX6rKws2rRpA4BWq61ertFofnO/9vby9h5Jaszkb7BkMzIyMsjIyADuXd8OCgrCxcWFkJAQdu7cidFoxGq1smjRItatWwfcK65msxm414IzOjqayMhIWrduTWJiIhaLpVb7Dg4OJiEhgczMTABOnTrFyJEjf/PGtk6dOqHVavnyyy8BSEtL4/Lly9XF+afZaqtDhw7VowlOTk4EBARUX//X6/VMmDCB0tLSOm0zNzeXjh071uk9kiQ9WuSZvWQz3Nzc2LBhA3l5ebi6uhITEwPAjBkzWLVqFaNHj8ZisdCtWzfmzZsHQGhoaHX3wJkzZxITE8PGjRvRaDT06tWrunDWpHPnzixdupQ//OEP1T3g4+LiaN68+b99j1qtJjY2liVLlrBu3Tr8/Pxwc3OrHgUYNmwYkydPJjY2ttafwdChQzl27Fh1h7e1a9fy3nvv8Ze//AU7Ozvef/993N3da709gNOnTzNs2LA6vUeSpEeL7Hon2YTk5GSWLVvGwYMHlY5SJ6tWreLFF1/Ezc0NvV7PqFGjOH78OC4uLvXansViYcyYMWzZsqX6jvwHUVZWRlRUFPv27cPBweGBtydJkjLkmb0kKcjHx4epU6eiVqsRQrB8+fJ6F3q4N/S/bNky1q1bx6pVqx44X2xsLPPnz5eFXpIaOXlmL0mSJEk2Tt6gJ0mSJEk2ThZ7SZIkSbJxsthLkiRJko2TxV6SJEmSbJws9pIkSZJk42SxlyRJkiQb93/kv2JY3dV03wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 518.85x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(iris, hue='species',size=6) \\\n",
    "   .map(sns.kdeplot, 'petal length (cm)') \\\n",
    "    .add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185438b4898>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "iris_embedded = TSNE(n_components=2).fit_transform(iris.iloc[:,:-1])\n",
    "\n",
    "pos = pd.DataFrame(iris_embedded, columns=['X','Y'])\n",
    "pos['species'] = iris['species']\n",
    "ax = pos[pos['species']=='virginica'].plot(kind='scatter', x='X', y='Y', color='blue', label='virgnica')\n",
    "pos[pos['species']=='setosa'].plot(kind='scatter', x='X', y='Y', color='green', label='setosa', ax=ax) \n",
    "pos[pos['species']=='versicolor'].plot(kind='scatter', x='X', y='Y', color='red', label='versicolor', ax=ax)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x185434d0898>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "\n",
    "\n",
    "# #############################################################################\n",
    "# Generate sample data\n",
    "centers = [[1, 1], [-1, -1], [1, -1]]\n",
    "X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,\n",
    "                            random_state=0)\n",
    "X=pd.DataFrame(X,columns=[\"x\",\"y\"])\n",
    "sns.lmplot(x=\"x\",y=\"y\",data=X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "`dataset` input should have multiple elements.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-ba20883c2f3a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m    \u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'x'\u001b[0m\u001b[1;33m)\u001b[0m     \u001b[1;33m.\u001b[0m\u001b[0madd_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\program files\\python36\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m    752\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    753\u001b[0m             \u001b[1;31m# Draw the plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 754\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_facet_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    755\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    756\u001b[0m         \u001b[1;31m# Finalize the annotations and layout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python36\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36m_facet_plot\u001b[1;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[0;32m    836\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    837\u001b[0m         \u001b[1;31m# Draw the plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 838\u001b[1;33m         \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mplot_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    840\u001b[0m         \u001b[1;31m# Sort out the supporting information\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python36\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[0;32m    689\u001b[0m         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,\n\u001b[0;32m    690\u001b[0m                                  \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 691\u001b[1;33m                                  cumulative=cumulative, **kwargs)\n\u001b[0m\u001b[0;32m    692\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    693\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python36\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_univariate_kdeplot\u001b[1;34m(data, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)\u001b[0m\n\u001b[0;32m    292\u001b[0m                               \u001b[1;34m\"only implemented in statsmodels.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    293\u001b[0m                               \"Please install statsmodels.\")\n\u001b[1;32m--> 294\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_scipy_univariate_kde\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    295\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    296\u001b[0m     \u001b[1;31m# Make sure the density is nonnegative\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python36\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_scipy_univariate_kde\u001b[1;34m(data, bw, gridsize, cut, clip)\u001b[0m\n\u001b[0;32m    364\u001b[0m     \u001b[1;34m\"\"\"Compute a univariate kernel density estimate using scipy.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 366\u001b[1;33m         \u001b[0mkde\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgaussian_kde\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw_method\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    367\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    368\u001b[0m         \u001b[0mkde\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgaussian_kde\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python36\\lib\\site-packages\\scipy\\stats\\kde.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, dataset, bw_method)\u001b[0m\n\u001b[0;32m    167\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0matleast_2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"`dataset` input should have multiple elements.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: `dataset` input should have multiple elements."
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(X, hue='species',size=6) \\\n",
    "   .map(sns.kdeplot, 'x') \\\n",
    "    .add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 显示图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline \n",
    "from PIL import Image             ##调用库\n",
    "import matplotlib.image as mpimg # mpimg 用于读取图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9f0028c88>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O3jupCcoIRzYAdhwZbFDFNZeoOdUoxBeoeO/pz3gdcrEKHvfRUqvwi8tQxd7Ca52fq2drgIpZb0I1/atfVS6T5Ei94TAIZKKbCECkCQY9Km8iKICcXA6Iqtf1lptAeeUijBNP1EMfzo5xguG+JBhsai8BHCkDZjdCB4VgnLrmeQTvs8SwlEcqmxFkY+o7rHmzYbDehvt5gcGlD+/dFxGRDmCfugrPhcdQTYXhp997T0dl9yc3h2XmM432DvcVtHX35CUREXnptqmqp9gmRqAv6hC5RKTswqFtMDJrqbcYgWWkgkO3KJuGZchStEYnyolhErrQ5yEj+Azb3rbhXJuBygz7wMNr6yqLi/OBq/B5Nnr80Ua7hjZ+8Ucb7Rra1bbz4kgO5qXMJqGVwlAliUmLTSLNEKq3CPN6tC6q7skpKk5zNQibF00AT7TAtJPUJ+lCaE5zXsO8FOFjA4BGaULTxUz/lpLUEfHfh2eBanCFsPPyVEPrcwP0aFAgWiwV9J8nGqpnBoQxfQ1zCWT3xjRaaSYJHdp3HVKW3rCuNIib2RJyaFm2ZkqQrEctJvfiRM89z8I1b8CBQFrudmsw4dgU/9Yi/L18FNKJe+9piL5Gce/WbQ2/M0Nwev9SQ9wt1j+/DC2uDjvZoo25AMhpY5h4Fxd6j3O0Xh+Crdgl4d6/dEdD+3d++/8TEZFDFDRfuR1AVz/4+bsiIvKJNzWtOr55LMFAnInUL+1xPUzRlvePbEs55z5ceM4KtlBj3X9kGabwjDHEJ3tSlpn0CulDB0bnyUSfk73ZSdhOf0+ezoH7pI0ef7TRrqFdbTsvcpJNconz8CaMSXmNN5UnRNSsR/phcqN5wGCnhpWmRqEv3lePnSfhbVmDXjvyfJOqhyOcVERE4P1YzFnDc68MrPdyqcfNYsy9hf5+/zJ4mLfPdKquw3rbbWhR1U63XQLi+sZNePEkeOwIhaYEx7r1Wmjaz4PH35/r234LsNHaFLwWaGNegLM+gjfKjfdwOI4LiDFs4N2rbTiPvCQ8Gr/HZu4bbVGCWupWPX9lJipXG/1sgVbZPqfcTOSxwj3ziEqc4Y/bou3VxeqZK0Bv61k4jwYFrnceaqTwjYfq8W/fDsfKot79RKOyL733VRERmX0UPOO/vP+hiIjc/ZL+7bOf/tTw2edefU1ERN58Hcw9GSfnjMAJHpE0Arwaz05chmVYdGNwlk3NRCYiWALSEizUVPZ7Ql4F8Efi3KcG1pvF3Qv6+9HjjzbatbQr9fhpFMvNyUxKg8Jg143DORQT6A1AIib3GF6JGbyy7ahQBonMtbVhY52kGgWs4OE3gwRWWJ/twy3eoO9/oDnq0rDjfBNwyzVadO/C079zZsBGmI3/xGfUa1wuQhvu2x98JCIitw81L3vlVfVmnakjnJ7pfsuJcvWdec6zmwGaV/XEb9zQZaYm7wXlnyQ4/3NAZ2vTKmM3dQJgU9vpMVab0HrMc0B24aHry5DjE05MIYxFq/s6XYfrsIT3z8A2RADO6SJEQBXy9m4YEgqRSwXOxfWWNQu9VvuHIaf9+kdfFxGRtwD8aSFQ8t5puObJtzUKiObqsQ9eQc2jCc/HPRz//Y2e12/95peHz/7suxopfPJ9rVHcBSz3+3/g08My0z0ItOC5YjCwdeGalSWHg9QiE+Wx3Uz2JYfnPDJRFoeU4n3dwhmG0PZmRjUo6sW5EcAz2mijfYyNX/zRRruG9j0g28ylmITCBimRUrS2KDFab63WG0J8ElCin7Y2iLkU4pY+QmGlMMUXkHI6IMy2QGaltpiE4svvf/0bIiJy71yXeWDC+LeBnttiv++tkHKYnGMuEDrwGpq2cQhNb7+ubaI9FBdP7rwhIiJ3TwLV8gdv6/7fv6cEjsRtn0oI9auvfVvP/xXd/9y0+o6AhEwxqdYKyS5DGB9hQo500DGovBMzI+6BykswnRiZdIKCmhgclKLXc44NgpBilSXETyj+uenCedz/UMPwlmrGBmfuJ+BOgDbc3bsqBlruB7ryJULiy6/+oYiI9Cj2fu13/3BYxkV6rXogGP/qX/4JERHpqvB8ffQt1YEpQZD6+x99c/jsXbTdEhSkfa0tv0e/FYRWfvDzb4qIyK3bEEUlFbmBhm7WC1wPfebqyqayuJ78E9GrBoUXY+aCsx8piFELI/6RTcor0c4bbbTR/pTa1WL1o0ikLGRtpsDKlm87yjEBY25fSRQTQAFuAQxzFxnJZMzh+1Y9RZSEz1q0WbaQyegAkLi4CIWmB4+0JfTBA/35u/eVOPHMQJ/vAOc9nen6p+9oIa41bcWztX721a8q0eLWvLXfePUlXAZENU694c03XxuWmWCKa9n/np4P5uttBLSE3PcpqJbvnwYA0QGkwPaA8T/AdFpsLugEU3EHKFT1nFE31U62lCq0lHwbPDXbqmSK4ey9tQxRxRQS0MutRhff+OjDYZkt2pBsS3ofHkeX6H5vAExz9BKunWnTHh5rwY4TjAtco5dfCVj9H/jCF0RE5Nf+91/RZS50EvJTd18flvmhT94SEZH79/R5+FYTnot7l3qP78a6TIIIxlJ437vHgqye8+ERgVkBrNThmSUtdmHas9FjkuQd2tcWJMSooCcfAIBdNso6OTiUb8UvJpM9evzRRruGduWce5M0l/3MtIY4Gw/AClsZjWEfoegGtRT6GfIdM4ae4q25XSGvMjx0grdihnx1DS9cmRbZI9AtX2L+m9nZwV7gqnvjNc0z15jbfnihXmxp5qbZlvwrf/nfFhGRX/yFXxg+uw/PdIJckECabRWux/uINPoI9Qe01SZmcs4d6jVr4SHm8OoiIi049r79/rsiotdcRKTMg/ehhyyRD6bIKXsrbwX+OrbaWjMp5h0h05wmAz+AgR4X8IwcJrt8pFHBzHi6JIUE15q8fMGbx5F6zwzAqgfv6OTdm594Y1jmc5A3+72vvS0iIqtzjSY++9orwzJ7eGj+4r/2F0VE5N6Hen0Lw7702g99TkRE6q3m7T/0mS8Mn9Uf6PFOUsKatZ5x8yBc8wK04Odneo4OsixZHvZBjrwIx0ORGJEAx2V+3pvJPZofAENUONVnZjYPUdpyfb7DqfAsGz3+aKNdQ7tajx+JlJN+hx23j0t8Bo44vC2bLry5KAzIv3lUS72pEItTD5FguKOpw/rbJQUkkAuCTbUx/GQVhlH8TI9nj+yqE5OL4U38MgZOvvWOAnJaI+U1A4POnVvK+fdj/8afGz4jKIZMsW/e1O1sTsNwywaDJ1PM1TclvLKRjjq6qbntDLnkjcMQlZyi83ABoEyEnHBm2GD259px2EMVn9Ji3nh1stq0nsIWBlaMfPP8QqOkCmyySWo6Kfj3Rw/Vi6ZO93k0DR2MCrWXY1Tum1WIfFaeA1F67y7PdTtvfSXcsxYsNp8A9Pn9D/R5eHj6cFjm3iP18InXa3+MCO7YXLNzeN8TCHW+HIIjafc/qeecgq14qvuazsN5zAsKYVC8g+Iw4etFIZBY9LPS8BsMzzoiuL5nBGVqHmRGzvR6pFO9DrOj0OVwUSYv6stHjz/aaNfQxi/+aKNdQ7viUN/JbJJJ6QPogBppFGdwCHOc4c6mphiLfBnCttKAFzzUZJcoUHlLHAmiyYuFhn0ThHuNmSY7rgki0RCsIH+i0UgrU+K8NcQ8PtEw6/JemCM/AFDld76seO9yP8SNHmSUU6rknuvx1CZlmRPnjeJkDg6CKDf69gj7biF8LIw4xH3g1g8QSrKLV6YhtMyAAY+RHjUUGDFUZg7/nqDgtDKzD+xeJgzHM4S/ZnKO03l3vF6/c/IuFCYtApV6TvLS/ZBWbZFq1LgOrJ92RqQi6TVEn7RaZH39jqZAvWlxXULkIwPo6wuf1nt201zP+UZTFod97YesSGagNzuYa0E2Q+qTRSGtafGsETuT4LM4tqkPnmEsY9uzTKv8oC4DEZAmPBckH21E0+QO34+VAbHtT6dDEfF5Nnr80Ua7hna1AB6vMM/WgFr6iLP2JMAE0MGIGtSYFy8Ad6S8k7diAnhbH+7p6/rMeNEmUy9RzPSzDJTRRRLWL1Bo2yvVizw8VS/SmldjghZbgz9+3x0FdVSb4PF7Ue/hW0xjGTLEVxAhFHizN5Uue2SYb1r88xa84R4KcbUhgMwBvMlQ9KyN91msQcqIdhjJTBMz+bYHmS0qEjsIUvStKZxh8o5/Kcyk2AptzMW5Hj83nZipS4pkDHP1+NkY6O8hIiiHyGFiCpDzGBBsFnLxs98zbUWAv4b2F56LGycBwHP/vhZOWZDcB9PSHWeKc7ivuOQyn4brefNVLdKmTp8LBxAZocgiIg2Ko4PKFyOh3JCXojhKsQ/fP6WQiu+F53UxM/8E82T9/s6+unWAYk+SVqJxOm+00Ub7OHuux3fOvSoi/6uI3BbFtXzRe/93nHNHIvLLInJXRN4Skb/uvT971rYicTKNUikmhnaYvG9IRoc3mfEMHeikmedEg+a5kVwChLIFgGfPJGoZPiua3e1MzFDKTezv7Fy3eftAPcLasKAwX+zYXoSH+YHXA+S2KHS9ArWKz7zy2eGz81NAO1MOHekyuYEe9xP92x6IClgDiQ3gZAPPcLGi+EeIbiYAyJzcUYDJaqHRyLwM7/gM0VSEWXvKhCXeLoOoCl6nMtchx7GRgScepsyDh7pxpPl2DpmufqHRwcawFW3OQCuNWkWbBW/lS70mCR6IAwznRGKpr/Vc00I/ex31nT2w7oiInIObborzp+BnZ1iLDo/Ve88w2GWhspJUWF/Pmc9nZMBKSUN+B0QlgJ0nBibthOxRiGzNtR6cPyDLNZ4zZ64nI4UGx7MEv8LUuO7jvQNJrNLmM+xFPH4rIv+V9/5zIvLnReQ/dc59XkR+TkR+3Xv/KRH5dfw+2mij/Smw53p87/2HIvIh/r1wzn1VRF4WkZ8UkR/DYr8oIr8hIj/7rG05JxI5v+M9CLX1gIauUT2OTFWf+RnTF0YDaRre2s6xwqzLtoZJ9/gEgyueb110EkxuHKGKfQAAz0cP1RvFUcgFt6j8v/W+wkd75L0//InPD8skyNdOCk0YC5On3WaEAeBKmWjeGBtZ5w7n4XFrajDQlElghd0CCtpcaoBlax37BeHJus0Jzic11d4spigjeAoJlzYDSS0AUBtUnweJcgmsuCnzVQBQGAGIBD7DFB2MGboM5OsTEUnBVkQ/X5khoQmERalXtgAfYDmxkF897uMZhp4AnZ1PQ1RwjDbJhEAxnHtmoj2O0Q7Pg2EtJnCJUNvh2TH3NY4pqMncHHBjM67dU0wV7t2KuSSPcUlm8Mc7nJC4SiXufY1OVWbEaV492pcs+aPz+IM55+6KyJ8Rkd8UkVt4KfDlcPPj1xxttNH+JNkLf/GdczMR+Uci8l967y+/g/V+xjn3Jefcl84Xq+evMNpoo/2x2wu185xzqeiX/u977/83/Pmec+6O9/5D59wdEbn/tHW9918UkS+KiHz27ku+b5odPXiqy7K4lwAjL50NWVA8YShIwQETSkWIUxmuZXko7vUohs0QruaYR7cAiQpFI4pvpAiZvSkyZlBRfeOuTn+dXip+fG9uXmiYW59lmDgzrctiaOngXHvquxtBDDCz9ChcRhByqGvbptFQO0P77PMnt4ZPajDNkPLaJwr4mMdhmizuwLyDjlSLYl9r4s8CwJsJp+zMZ5wX71AUZMg/OQzh8wznTYERtm3392+YcwVoCnMJWyPakSZ6/26c6HGvV1qk7Psw1cZiWIZ04uhQi3yxCXeJn/cAZrF1VtlYG+eR4Tz6KNyzPN9dzzSQh39xqo4TjB1SqCh0/Ab14cHVmjkR6uL5IZUFP4JJz/jsU7Rju9bjt+2722U5FEOfZ89dymky8/dE5Kve+//RfPSrIvJT+PdPicivvNAeRxtttO+5vYjH/wsi8h+LyO86534Hf/tvReS/F5F/4Jz7aRF5R0T+2vM25MRLGndBKF4CaKGhp4U3t2+kml6DBY4pIKcGEknetg5urDP7yAEM4Zu8dhQ8CO1ADktFmNQitmdlioQRoKAV5sdvwXvZybUYLT5OCRKcIhLgyBGpwH2OZQysF8ucodVVoBWZmDc7cTY3b6k3nBsTj1eOAAAgAElEQVRY8QXANQ1ALSmKWU1rWHJwvKyzkR7aetw8QzuNrT/DfJNC1ipBcbMHBfhyFYBMQ6EMx3oI0FBtQD4N7kNNqKuRRGOLkDW0l166LSIiK0PP3aNQ5kjBAPkwRkkiInmEe8/pRrbYTHGO9bE4ASzWh3ve4t/kNYiiJ4tnfNbIS1Bigo+8BdaGlnRkIgacQAtAVYmiZ2+uFSf2+g5AKq/3KktClFVmbpjGfJ69SFX/X8jHC3L9+AvtZbTRRvsTZVfLsiteEtcMvGMiQSyTf0kjesPwRowwx54A6DEM15jX0RwDO1u0trrW5sR4kyMH65EHMUcVEYkArGC7KAfkNaqNwCaWn2Tw4oguLJ+dx37zPN5ZRyQMGfUNWYLRMjODJ1mhntHTwyNKcUmIHKYYypnNESnEIe+dlbpeisijd5TkMtcDUUiPlqljJFWEugjz1SzDMRtGJA4psXYigxMNx1iCBTauucyu2ISISJGiRVngfph7FmOoiMIX5xeYmd97aViGMGYCblLk883W1CMAmY7ZskO0U1hGYYZ3rA+ZOXgyQbG92g/XLuwjcrsQW0YicR6iAzLj9IxMDctOF/N5hrAIIOHOgHEoM5Z2Gt15nHtvZM9c2u9c32fZCNkdbbRraFc7pCMiXe93uNla5O/MRenFm96MJPLl2lNOGHm8qcyScaacKVyz8ZVZH29iAni4HfN2TCDL3SJPZAQRsmeRHPm2hxdY4+2dmcvoAaVkFdyO/tbg6uMwzGymAxfZPBxIDRhtmeh5eAyFTItQB9gDQKQDAOjBRWCu7XsAbqAZwGZAbhh8UPCXGLWFCqw9c8PfNp3q+pslhlxMtZhCoBVHS2Pel3CMywsOtYDfb05mpACucYDobuCx84npxNSUzqJnBnvSOqDCM7Am5ROw2iBKivbDXRueAogAUIZ9W4dOTEItBwYnhkmI9aUo2n0uLJ+eBYJZ2xkNx6UNHPohOkpQkyC4h5GD7QhVW3xPcB1Y5U8Mk+/larHTfXmWjR5/tNGuoY1f/NFGu4Z2paG+x38TE9IRrEC9cUpGZUakggCHXjjBR1rncPgFWlpt1+4sIzKockld9TufOdNSIWsLZbooz9WbNhplkHL0fyJIYVlMtUfRpwZltjdAJB+B3BKHzSGwVRPSkn2AUNwFmVZA8liEiTMHTPoK+vbe0HtXDVp1TkPZbErVW9Mqwz9jTuchbK2aQIJadrskn73B0ZcQy2DnqEI605gWKtteGQlGkQ6khuY7wf2LEhbOho+kmJGnQa8ZW41pFliXmBcCti4JpgbNYQzCICW4GLiv1szDM/z37AuaHLD0u0q2HlN9Vok+prhFTBESAnBsCoDnG8+TVS8ewv9+dzqQaaeIDf/xfKHobL9L5w/9QCX/PBs9/mijXUO7cgaetvWyWBoBR/zklFGDYkpeBI/PsbwerDot20aVadvgTTiw9BiP7+Ca2G5yXr16YqIKzsbHjiAOtLNMwcoDxsoZddZnMjO5xhduDM+yXdvWJQpugAon8AKHuWktYab88GiGYyYHQbgcDSbvchS3UgN8EXjRnACYhtcgnAfqVDJF+85huszQG0gL7XqHwpMVYxwuW0swCoVOQ8uvhhfNZrrRTfMkmKVDwYrtMHvP1vCsGQp100KvR20BVY8V1apul1HI7mMLrzrb0xZiefAkbbpg/U0T2qO8tEOUGVP0wghaDoVTtAOH58NOjzJyIEgnTFs+bi2mV1Mzedeg8sgIsHnK9fy+T720I5zyLBs9/mijXUO7YplsfYvZOYKC4Bz8LcdQSGcBEpR6godjVDCdfPxbs9t526L1gRcz8/Cd1hK8ecRBDQBY6iYknlmGY8NnHK5pjadL4FlbsP5uN0YgFI79fAUmXDDyFuY2tIPXoquGNzaw4JagIrK7+uD5yPPW1xyggcc1Qz5JTFgxhB+wTpGZGXV4+oH51fiIFOCmEj2qBLmoHfIRtjUBoCkQJrSmjTWZ6v6SjoIUZn49J+QXkQMGu/I2RFfRY2AvinjmBl7MmtFqo9dzvcFgleGEgIaLlFPKZIX1a8J3iYwZGJ8NnJbPGg+ZsnAGgOPYqgP46vTcMDODXagCJ4TD8FFnoopty1BSl6lQ83j73XeGZb7/J/6SZMWLfaVHjz/aaNfQxi/+aKNdQ7vidl4vbb+VNDahFFBjDUKnHPHw04oXbGlkQO41JsRO82xnWRs2DjEYQuO6Y6vNLA8VVAa2TCOcobVqSCfF+XVEe71Zhnhv0lCVZQhNO6Dq5mjHJQ5tH3Psk4ztN2DEiQ33pv3DefGeU3ImDcDEIYtQNVINq9VeM8xFUbHekubaTIwx1B4oo8Mp8j6EdtOTxayB1LLZnUZLTAWxWqKIBlRib9CaaaupQYEiL3USbRuOaM8YhdkGxcaqC8VjPhdHkxLngaKnmLSE8xQbPZ6hrScBb59g5t9Ty96ImPgBcYd5BBblTDrAUH99qSG+M+kZU88e9zweUhhD+jnMeejf7p0rgvFrH7w7LPLnzy53VI2fZaPHH220a2hXXNyLJM1zSc00G/8doQjDNtgu+wjVcSk15J9YhgUeTqXtTHrFLB5BURerXV4GwMpqucTx4JLAC0yzUECkt6Kno3/qvAUC4U3u8PaOTREIQJ0MUlMt2nKRAWrwfc1aEj2DN+rB1RYebXC4JjrCch0jBYf14rBMsa/eb3aCc0MkZI814TQgVut32qOcDtRZAwJOLIkqgTKcqFxC+GE2NWSZ8MIJPLblNSCIhu1RelyxnlZ2nweSrzpTGN7W6mGbBVtkeu2nUyu/hggSRVM74cYZ/RrRQJyRkccQcqIQyiKf5+yCicR6bPvtb78tIrvt6qNDHEtfYf+IwExxj6xN2zWmLbFMV4dlTu893KUGf4aNHn+00a6hXanH7/teVouNeAuKQV5HzXbOfa/XYc54NoNwAl6gWUxRwrCdFrkkZ5jticWeb9AM29H33dFBaF+tl5yJ1mUiAGFSM1PNVkxEDwXvWhl6bHKgRchbM9MaKnpQTIOPj2jg3kzwxfBsNSisCWox6bPEYL5ZbtSLWsaZGlNnDdpXZMDZmwVo5wR8hJwcpIeMnN0OPmufnLzjFBkjMTrYLA31DIcRwAqerkb76dzQa5/cOMa2sV8jaca5ef6tj8ipECKXGCGPH2o4kF+znHu4/r6lmIquU1WhlcspOsKTY3McPMcso1IonqXY+kxGGODeG6TJzBQqPDOn7GxYsRnuI7cDyK6RX6sb8vnp7zlaoK/PA4dhtO3Fli6eZaPHH220a2hXK5PtnEyK3Eguhbcr8/UIAJjcDsI/VkWPe8g7mcPnG5ggCDu8wOJqDSgmc31n3nsOHom5cQwhy9rS/MB7MsfP4IWmmYVJwgvB09hZbdYouAy9ieUnYGV8EA+h6IWJbuqWjMK6j802VLHJXcjkmDWP1IBaKEhagu2nBouLM0KllFsmTLoxuWOB9Ti4w7zSim6QWYnnmMJTnq3CPH18qfu4UagE9WQa1mehfYgq4Opi00GJhDyNeD4QCXVm2occiBTN6Non60NZweeJHZQQXkV4Dhphbg0GHVNHGGoNZHji7H1vK/d6/nv7ypNowTl1jboWc3tGtOZ+JKjjcGhs/4bWV04eBcGXMi1emHNv9PijjXYNbfzijzbaNbSrBfB4L11dy9YWsxA6scASQ6u8ty0yRFWkGWLXKTdFHIavMULa3LThuoHei+04rhNCsfmeFhDDmDQpksKxMhTmHD/TgMgWpVCg6bFti/v2CL85KddgEs+SO/JYE5BcbqEl2JrQskaqcX6hmP/lIggbRY5piO53UpIQ1BTucP3PoW8/4xCBmeVmsZSAkNTg+NsBXEShE9JaGYES0qVDGMMBNFWkIQVbL/Rvp6LHkRoCzAmKvQP+vyEFtWldMlzHfS1YXDSFNwJ+SNteFqR4s6ArFDd5jSyQieInnLhDy9JnoR3H8JrFvOE5MaCnFMd287YW47yd7kNBmue2WmnqxklTkdBqJLCt5GRm2IVcbFZD4fV5Nnr80Ua7hnblZJtRJHJ4GOScIrz5SNnM9svTwDnlBNBOFJw6ozEuPSGZYKdZGR11FuwIhezoKYKH65Jdqukopo66kWzCK5wSYOgSShMFLzQhNDTS89nWoS3ZUOUXhR7CWhtD4U3oMkU3IhYJu9DaWUNjvlpC0dd4wRyMNWlG0RHdV2kkxdIZYb2IknqqswZvwdZchEJTYmDWbc9iICYAScG94+FQTEM0cHiMefrTcB6coKxwjdebcK32j/ZxPgBkob1ZdeFc6d2iodUH0JRpS5LMlXWy7QYRQBrOh16dhWXLNsRrS04HPotUERYRieE/c5LA4hhr046Lh2iAxdewDxZ0e9KEk3zBRHksjrYAYt061lboVwxEt9lsByLZ59no8Ucb7RralXv8VpysTfuJAyZpxBnxJ9slAoBKUz8GhbT02CnffHx7G/gpMiEOznCAJ8tDnkaARJ7vXpIkCnknPQMBJzHm2K30U/BC8Pxt+IxgoIyRB3J1y5KWMm/uyIqjx355HmoNi3P1fosL9SjzMngvwooztKGKIhw/jfr2vA454bDmQGrm644cgsbD4VpTHGLgCjCXbhjYcWzHgVHHtC5jtNE4/PTw0cPhs+Pbqrpe4PwTp7DWZLI/LEOgTN1opJBNIKZq7mvueG12+RatxBrhwczRLQaG0dBgeOYmtg4wbIZtPb0vZfE03kdwKJgaA6PUmLx62HZruByZ25Nr79YNvT6p2c6Ddz4IXA3PsdHjjzbaNbSr9fjOSRQ5SY1Mdoe3NmGWhMVaQUzH/BL5S8dhHVtxb1nNhwS2GXzhEAdf0kGCy4pd7sogkSXWkpZGlFzGoXUd2VDDW5brO1C/UhhCxMguDcSzbmcdPX/8xLnyDf/owemwzCUENck0UxjOvhQgmkmpOTVrDtNpWIYssIQTp4hgGiMJziiGQ0eNFXlEVDYMmtCbGW9TL9GxwP1g2jqbGnZbRAEdvNnWypajm1Gi0k9eQCORIQ5sNCk5GXF9F0ZYM7Av7455pwaCTN0LAqkslV9FuXFcGocIIDELMXpYQWikQGS3XYcoaWDGcYQOWyg4AERk8Im53XA9e9QtIhx3y+esCBHQqvcDE/XzbPT4o412DW384o822jW0Kwbw9NK21c4UFwsbHiUVhn+xob42AbD+f+jGhXApAwFk17CIFNZn2sACCRl8YgOcyQGWYLhKYs/O0vQQGOJ3W34W487CWU5tM4PiiKG9V/cMTXdDVJFQcGsAFCGA5/7994dlaoSfR0eK084MmKQsIRzxmP7apQl/Wfyi9mBBwImdmhw4DBRI0/jAXcBUK4sJaNLrMDEtsm4NUMowFUf8uZngA3BmAoJVvzofPlvcV0z/bE+Lej2WcY1VH9afKVqVnIWI45ACEtiVl0hdyO1g1ZhxbCRYtdN9JPSMmW5iViAyJUCH55KAmw6pn2WB4vkL5kwiQ3fOQ+l6MADhmduhVIfuIsFjEWb+Hz26PyyzXW4C8edzbPT4o412De3Kp/PyLNvh06NgBTXFB7EKw0nGtk2Cwx3YR8zLLYp2Z8Q3a8PHB7jpAI0k6MEypMD7pqTQ9hQwCDvxQ6tOj60C4MUKWqQo3rQE+xiBg80GICWyy+BNb6cEN+eUAAPHHJhftovgofbAYnPzWKfaDNnQUKDiz/gxCmp7TmxVXZ4pS00xCZNeyZyRg16j6SwUkRpEIdSnz3GOlhcgy3D9OnAqckbd3FfyALL+6cxkGVuda8CS5yd6rjbKq0EPtF01+Ey35yw/AcUpHgPgWON0HYt0lmeRURqLrQ3bcab4zOeZBcMYrelOQuQxRLKAUls+PhapNwB7xfFuZCsiUjV6rSdoN5O1KDMqytIuZRghfY6NHn+00a6hXanH73qRy3Un86nN8fUn54y9kPcsvFELeNQO/GeUL7L6DSu8rXN4kdLID9GbU99+ENvorDeHR4oJp91t64kYTjXsmNGKMyCPZgAJIe82n00gA0XuPc6Ne9t6xN828PRLDLL0xq2XGEAiq8tmG86jbdVrDLDo7SX2ZeoIiGoaIkPBxNuZdp4HJLVEO9D1pvWJKMBD5KEeYKIhyiKDEq8f8127D6bJhLpuM6Nrj4gtBmjLr3DNjKcljDWL9RgpRrKjPT/Mtu+2Ym0e3w+tvifpa8jkPAzpkIPAzNqzBc3gcAD0mMiDnAVNpbUSws9FRDzkygjwItTXCoSRcy9D63W50vvaGJDPNJuO8/ijjTbax9sLf/Gdc7Fz7svOuf8Dv7/hnPtN59wfOud+2TmXPW8bo4022p8M+05C/f9CRL4qIqwA/Q8i8j9573/JOfc/i8hPi8jffdYGnPNSJO1O+JwmpHgCCq7jbHQIbUmwyAINw1ZbDGqoOUfqK9Niy4Hecm630NX3YfKugjorEXf+MXqsnfOIiLgDjtyb1mHEaToQL5ownkIeE7TfepxXuzUCDhSnQAT34AO0uLpwrtTny6C+2+Vhqi0BYWTT6jwE0YXOFMUqIMp6kITO0Q7bLIKeW+LQxgNKb5rvmfVRcGRIOrQOQzGLmHFOB7LlWZhW7oDc655Es9WY2FuerXY+mx+EIiOrXy0KiBQRafvWLEPxD+4DpKxmqi2QhpJ74EnF2QbhOKnQEjMB2IP4MoUoSy0UbDGcEjgkjzTPajKSSFR6XjMUG016tzfTdCbDDMgSU471MqRXk2J/57v1LHshj++ce0VE/h0R+Xn87kTkL4nIP8Qivygi/94L7XG00Ub7ntuLevy/LSL/tYhQheBYRM59AMu/JyIvP28jfSdyeeFk2gcM8xaFsrJUL8jihbfqrPCQCUU30icJJD0klyhOkZo3H1tA9CzDrL+pDnIqkLh+ShYliY1Okp1lIgfPXRugRr+7viVM5Gz6BuIODgWiyIf1Ly8VuHIJNdXThxc4jrAM5ZtWC/WGlQ8MPIyYupZMQChqmVn7nmIXjEamuB4GwJNRSaPB7ENpeAlQTGw6TMWhMLu4H6Yue9kVsJjNNGLYJuHe03iJSgO8yeFRPRVk4Q2bTQAipVONSuLhXqOQZ6IK3uOBHYfKuqaw2z1God2ayIXGaILF2sg8n2RZapp6Z1lLtEpwUMO2nmkTD4ErW8igNl+vwzWPPFiGUOB+cE+BOzcxxSgi0m/iAVT2PHvuUs65f1dE7nvv/6X981MWfWoD0Tn3M865LznnvnSx2j5tkdFGG+2K7UU8/l8Qkb/qnPsrIlKI5vh/W0QOnHMJvP4rIvLB01b23n9RRL4oIvLp1276ZJLszIjHeF+44b2hb++qNrBLwCVrTJXRMXmTA2Xg6guTd+GzDdsliCbIw5YlBrYJz0RxhhZRSWYAOCsALOjFWhyPmBx/kqsX4tShrRCkE0Bll7rtBjPum0WAwzY47w6w3MiRujpcjyZ6JCIiW0BDSzEz950e7wrAlwrRSdmGyMNNECm1+t5fIBKJDEqatYp4Cu5AwysoaDs1K8BGTzVK6UwdooIgaMr2JGi1Z5PgjeNdR71D4UOPSkHJutJoIklNOw9cBzVy7AS5uW3zDpOZrHWwTWzYmwg8ouZ8Vhh2HmwsGcQy8XyYukwHsFWPwgwBQLaFOkjFAVLdGH7CgZOCU3r4Vk7LwJqURdoK/vC993Fc+gx/7gc/OSyT+1Im/+c/kRex53p87/1/471/xXt/V0T+hoj8U+/9fygi/5eI/PtY7KdE5FdeaI+jjTba99y+GwDPz4rILznn/jsR+bKI/L3nreB9L1W9lpUpPBbwqGRmIdBhbxqqyKyOZpCu5lvX5vECD13mZJUNnppRBefpuZ6VhSaDbz3w86ET0Bs4LiIPeqPJwAFoKrzMKelZjBfrANntG4h/QABxuwrevAfzbrfCcQBls+hD/ryPinuMyvliacAk2N/Fpeb9Dy40Snn1OOSCDoKe8xmvMbyYqULXiCY6RCUUvVTT8724UC/OCMpZSTNU4e/deyAiIX9++UaoyqcAVBXFbkQnYjj/cGwN6I83LqSLHJyh3NaQx5sjbQdZKnAHEnRlwpsagqnc3lNQvWafGPaJDfjLU+CE3HnkfzDXs+EyiAZMLFitGNUBGIXaz3YbrscFqvenp9rl+cRrr4mIyOHNwF/ZVn7o6jzPvqMvvvf+N0TkN/Dvb4nIj34n64822mh/MmxE7o022jW0K8XqOxdJnpTDxJiIiKc2W0d9ew23VpsQWnLGfiYIcQnksRp8CN97ocpr2C/BOBGKQB77T0yq0AC4w5n7LNLCSm9IGVloqojrLxGOmtiy40w3jjEx02hD4XFoCeG6hNUlheBDinSAOJHc3KpqrdcsRzj80LR9Vo80NP/KfaXq6hiWn98LO8l1jz/6I18QEZGSmHJDT7XmzMEpCprGRzSyS2O9Rhvr0XmYDX/vfS1CNWgj7u8diIhIZvD8nF9nqzIxVbly0OfD3xBZX56H54LaicUUNGU4jmISxFRiFgORBlQDcCe0FROQlRJ0ZLA9QwGUR8YaZ2tUe9kejgGoqkFG6kzLsAJwh7P/WR4KiCXmCWq2EVGIjU3ReAXq8ckM1xETmrYtuY39MPPyPBs9/mijXUO7WrJN30vXVtIasQwWI1igiVOy2oTV8pjz3vp7SlCNpUhm0cSjJWOm8wqwyHDuuu53izEioRXDd2HXEIBiSBlZVAR81TmCQUJ44Un8SELQyghAYDa8e2yazftQxFl2aPVBkGNRK4BnWpu2IopJF2iZbUx0c7HWa3L/Urd97713dTtx2MfJXS30nX1Jf78Lb/zpN14blpli9IIgXjvxxhreBuCa5UIfo9/5vQ+HZT48ozyXfrZda5FP5reHZY7hzU/2VByiNHwALiWhJ8AxuNcHB2EZDshxkpIqs84+1dhHjOKi43Nho064eLIwtfaeUdqNMll4hiygil6X7EtRTqk2c83wXNUtJNFMITXHTD09PiHqW0NpTtb3vRN6fL0uvQHsnF2c7dLSP8NGjz/aaNfQrjjHd5KmbgAziARuOMIcG7xhUyM2SYYTV+8KbNp2HmmsCb2tmpBLEljMdk+EOoIzsMkac81OqLGu6y+Xpo22f6T7Eu5fgTPNNuTYlMNKgEPtTd6ago56fQ5wDWm5jfDCtFevcw6pp0NIJZ3dC1DVPez3AHrwbh7OlU7i9la3c7nQWsXC5IIOM+aTtXqde0v1xoVpx33+02/odcB6jclpCbFd4Rq//dFH+ncX9jHBfZ3C/b5845aIiJzMjoZlpNE22vZSr/HU5L0JQFJ9T+8HJh8jJRaRFxH1mck+ADBVuFYEZvXUsOfQkPHY5DeMCCIz14psSw1ydEaZlgmIbEcZaM63uL65eT6rgeEJyxqK+YHRCedGkFKShqjgaK5o+emBevxkMsM64VgLty9R/GLtvNHjjzbaNbQrzvFFoi4ZYLEiIhkEE+J0l8ssccHDpClFDDjiiZ+G4SQF1DcjCMLk3ekgDiFYH0AeM8xRgHk2BZw4BuMMJY9EghSXT3aZc1LjPRyARGR16U2lfLMlDNfvfLbD1FIDIgsBhl7UG26bEFW0D7Viv4JA5sU2DOmsAQqSrW77+IZ62GkyG5aZkmNupj8n++pFlnWADp9DkHMOT2M7MacYJHr4SKHDcY7hqWnwI65DdwYeiT9PKivMgagIXQ47DsvxUkaCM3AAuqdIYEcE9fhdZiAREZ9gHBe3gZ7acubFJYBY6LpYMRdCrxNqXQ6Q3xDJMZePibVFVNAYoRXOfjHKs98BCmn2CE0bRrZmHxFh6gAksVthQUKz/b3R44822mgfb+MXf7TRrqFduVpu17fijZJtg7DMPUYpvFyFsDONNczLgKUmgWJjWkxNomHSHPPjRRTCxoqEhEwVEB41fQjFEBHKBNh8Uh1nlgCS9NgA2bCAZzA60tcafhPIE/dh/S1myT3QKFNOqpkNbNHO6zF/fXJyR0RE1qsAwFld6vncu9Ttvb8MQhSXAPckwPjHCYuEoQ02RTs0B5/AQafHnMcBMHKx0PSBE2+2renAHlOACehgT89jbjT8XKNpwOlHqoDbgjS0PgmhKaccsz3F79upOk4pxiiIkl7a0p0TN9+hrTmIgBgSuJjAMLTqyHpkdRN7or0SzFKYKUNBgY1FWvIDpIkhy+QsiCMITddfG93ECMcaAYRm5wGGGfoeE40oAIYWs0gCLUQWK4dio7E8znZUeJ9lo8cfbbRraFcroSVeetftwBU5L001U485aRaVREQcvB9hmzFwk4Vp+VUN24DqderKtDnAKUcF3XKq26760PbJKXIBrXUCi7o+8Nmx5df26oWmKApZTfLe7xbwSJUsYgt+4HYDVDQ2bZuNBzsPCokECR0cmmnFVqOhRysU+bZGRx101FTZyOAxU+Mp90sUuDq2N3Hto1DIpLNabwghDtFAVugxHpXgHqCCrInkDo/BmpSoEMb8RD1WEm6rHB1q4fHkRM9tklnmGxR5UTjNU13RqtSyWstJQgqTWCg2GZASwGkJl67NPH2JyKumkIY9DE8pNLQFo639sy7e7rI3kSuvNNDhLSYPt7hXxSQ8u8TckMuwYlRhJMl8yylFgs+wrhFzqZvtABx7no0ef7TRrqFdeY7vJYguiohU8JYOnpJa75eXAThTQvM9Tyn2CDDH0kAaIarQCT2uAU9A8ontowb5e5GF02e7jB4y7tguMfBi4CaZN3rkYI25jBQ+2Na6vSQNLCprsNi6QTwEIhGGc4/1A+abexkAThLacWxrRvgsK00dAp6s2qBViBZbYkBCBPCU+4gGkBcezcOxslXGGXlnrgOHcyhWmeP+fHT5aFgmxRDJa7fQTkR9Zt9AbvcPNappWm0PNhLyZgJ4fM9Ze0CyDTOTf6xtRjET640LkEI34Dlk+1gM+1KLmoUD65AFmDU8fwqsPEWKK36szcyAw/reGDUBcguFkZUAACAASURBVA+6KEQcjFgKRKsRQw5T++kr1MCAU04AC+4M03SWZTtCNM+y0eOPNto1tPGLP9po19CuFqsvThIXS2S1zQYxBkw/ofgyKUJhZAizkmTndxtM8W8c3OsNVn+C8K5lSwb7t5RZGVBwbClR2da283JMeMVot3CKKzYoLAouEO+92oR0JC1QoEGrrxdSLYVlyinbNTiORYNzNwUrXIdyoqH2LROGX6LF50FXvqy11bdZhtBylpDCDEUkhLZFYWjCiVJEWrNnJueIgBxIUFHMevWVwLB+f6Htxzhi4Y2kqsFYgJ3i/tgwlWSfEeYHyAfgDOXVMIWH03fkZGgMLRYKkTNQufUUakmNiAlbdvhT35pZfbTmhEhQR6p2WwHk5B11E/WYK1P05bnVTG0jkyqkPG6gV4XLhn1s0a7OO2o66sG2dhYkiWVUyx1ttNE+1q58Oi/LUhEzxeXhdfISb00U3vpVaFHtzZVQcJh35pSdKeDVkFEiVUphWiGcMGPBqpyqp+ya4GGSx6IJFuBqO5uNF/AENNlcu9uYNzuVfKncaqbaYqgEJ5l6n0HOyRB6xp5kkKAA30M7KTJtxU7beWt4BB+FlmGK2W6y20Qg5qzPz8IxwlPPAbyZApzDoqeISFkCo08REdMO7EgjDe9JzxabWfApps98o+uzAHd6Go4jcTqxR2/uTcFroLN29KacoLP0OCzKgWa76nfWEQmFu8WFApIK3INeQiGTk3sx5jw6MztBqawOTEIefU5Ga/pvRGUk3cR1KPOwTIvPsqcUB9uGKr/6e4EidmSAROSkyBndteQFMJj/rns2U6ix0eOPNto1tCv1+F3fyeXlcoAkiogcHoI7DG/yCZlFjFiGozRSwmkuegYjqEFQED6zMkgZvAdhp13H2f1QR4gAP+ULM2ceb0QvSdnN2XTyAzjDBLSt0TrEsVEaTETkfAXPOFA7cxIxAIl68AFst7ttyUsDYSZEdAsN+7oKEQeZXTYAG/VoD+4fhnbgHuoWOUAp+2ihJsZjV8glqf1ezEKrbQJxyEvUUSowCSUGJp0jb196jSJmR+AwtGKVmMjs0bJLTV2HuTSnzeYAC7VdONcO9yFBfabAuVbbcO83G40ES3h6Tv353oqQ6t9ayoyZ/JvPkW84nsdPjOglrxuh4DiuztyX5Vbb03xOI8NCxfVX4H5gy47tPRGRmPk+YcqIEirTepwW5djOG2200T7erhay22sl9+IiyDE/LkTZMRowb+SIoJyIbD34e28EHNyudLbNoXrCWJGnJpDSqqIw4z5AWrFeFD2ZKzHPTTFYRGHP1rz9uVt+ZgFAxzOwuZISKIZXbo1kE/abTnQfywtIRxkAzmKtlXqWOLJJiCpyDDTFACdt4SGdGbK5MdehmBk8CiWjWyPg0KwAIJoDamtySapZl4l60Rny79Yw+HgKlCL/X59rdDIzslDnEH6MwTLkDHR5S6AKOi9Zy4F0w/IDqG3DISzWTKzXw+I1oLJVqudV7ocIqAGgiXyPdliIXHttT9AVnw8j6opr61G7IqtzangfKYXN9a38OrsAOSIvSnAl5nqSPYqo4i1PLDES7UlmFDifbaPHH220a2jjF3+00a6hXWmoHzsn86iQ+UEAxRADXiIMpg44sff6N4I/0CYhlVZr8ONdCItEAphDJNA1sbBCIM4OLTaANwwSo0FCwbT8mGMkKGohRLSiBgPXgCfgxBSKoFnH6L8jAaRpUXECkCCZDttjkUpE5HKlxcAtioqRmVIkBn0galwjtDU0Yzl4pCZoobJett4YfgPMe/dUFjaFJob6LKTOJ6AAW5u2IhVggc3nrLwFVjkMzm8AYEqsgi3bsh77RXplC8PekbgS6SJnHux8hSemfVfXrjdTkxxw6/kM7BTewKuA58DjWarNPHzHyVKKqTAVNZOEJIx9WqjP7iEBQEO325xHi1SlnAM0xZvmw3P+Yd1L09t258fb6PFHG+0a2pVP5znndiinKQCQoG3WYUquNVJAGZhierpBAl46A5UdiDQBaczNZNOgsY63NPaVmVZIMhS/OHlH4kUD1KCm+fBGxz5NESfGBB/JFDvTlkycFpQoDtGSM8BcD+9JK00FWrS8jLrqdE9n3KcTRixGVz4h8EV/PzrQNpgVmTgAoWiBwuqKXsLMdhPQNHgzMQVIss+QiBL7OjAMPFuKQ4DuvAN5qqlXSQ6P3+G6UuFXRKTAlGTPFiG2V0bhOGJKTSWAZHeE2hqFYtx7kmvGXiOQgZVJgvoxCSwjI/MVA4jUtpzSw76Msi9JQgfPjYJmb6brOPNfgSbcCj17RGARYLksNkpr1YN14w+g7Jvd0oLo/o2glntWbV/YlY8ef7TRrqFduccXtyuPxX9TqijFbLf15hFciuff4Jni3bRet4dXmWX5GWbJERbQQ1d9eOv3YWHsFPPo3tQBUGsAJmU4HtNhkgTdKrLs2OimatWj1YBibsG0kln+tscowM+XYGUxoBSKjcQTzMU/Rc4pZq0BPyPT3iSvwJAb8xD3Qh2ghqdrMAdeGu6CHP/uAIqpEMHs6NIjXx7yXOSvnYnkNmgDdg09v2lxpZShanZ+TjKbf+vyi616QQ4ZZVlocRUUpISHbrHPSWlqQAn5HvVnbyNJtiU5nENJLhMJMn9vmgXOGeIwJqpgMNUCWt6ZZ2+g/MOXgTTnG0PNTu5DB9ai+2tt8y4eBi7GuJhIP0J2RxtttI+z7wkDT2NHCZEHNcyJG4oT2BydnoE5Pt9XYTvk3yOsdrs2o64YWCGM1nM75u3IIZJBaOEpcsPJ4Jl1GQJxvDmOGlVWVmbteSyRk27WHDZC/tiHfQ18gBUFMTQn3RqZLnYjOM4ZGV87A+x1tdbogsMdtclJHTxJDXGLDECcyoiYxPBa6ZSSYqbiz9waHYsp2GDW2wAr5rDS4ClxrFaKyycU0gBzjemO5BA46VDNJoy1N+tXrV4TymttIYOeGoHR+QxQXY6+YqBmayKojAy2qGNEu6qb+v++3fk9TY1oB0K+KcBOZFi24h9chjx6FGwRCVHhCvpnj9C1aQ0z00Guz8UJuChzQMHrHaBaG9pBz7HR44822jW08Ys/2mjX0K401G/aXh48XEs3D+HI3fKGiIhkCC0z0kr3IXxtEZ5NoLUeI44uCqOuisWplpvkBouNMDcZcMwEWhjGF4RlLN4QSJOYIg5pj0njzGJQYpRPGVIPKrOmwENRiPVGq4MppuTS1JzrRucYGMHFoLyezyw7DkNjrNOEfWwulXJ7ipB/D6nDZRvmI3htY7TKSJ6ZGSWKaiiG6bZv7p+EfSBF4NXcw33YhEhfUmwzwbH2BPSYmYGeLTbcj93Wqd6HC6RcJaYD7ax9jfA5maIQiHDcOUN3HpPfgPh3FjTN7AHawwPpaWSqtY/NgDAltKAtFvdapnlMBQ3fA5/4FmlqZdh1ehT1HkIo5a1LsD/NwrRiXGjb7uKRrneIaceLC9OWLGJpO1ti/XgbPf5oo11DeyGP75w7EJGfF5HvE315/Sci8nUR+WURuSsib4nIX/fen33MJkREpG5aeef9e7LJw9vydqGeeW9fAQmcELPtpy0EJLZ4y3I22xnmGeqeJyhUbbeBnpuSWYSNNihuJc5MP9GLwvMfAGrKtpRIABBlLDx1T3oqv6boBtpohek5krmG3H3YTmIgt00PWm4AkHJ4ZWcKePSa/RqttjxMvC2hDe8qeGEUmuow8i8eU4EnKBwWKYtbhrtvpRECpxTXa1O4i3V5euoKraUiM6IfvOb4fWDOMbWnDjPuLBz2hvLax7tTkqTF7sV6agCxcP1It96byc7FEmq74HnI0fo0tHwDZXeONqedcPOeNOv6O1WDe8OTWFBtF1EExS8KAzbaCiM58keGz8id0KHgF2X6fFuJtx4Y5sMTbeeRYOqkMMIzcRI4Ap9jL+rx/46I/GPv/WdF5AdF5Ksi8nMi8uve+0+JyK/j99FGG+1PgT3X4zvn9kTkXxeRvyki4r2vRaR2zv2kiPwYFvtFEfkNEfnZZ21rud3IP//Dr8gyDu2vL3z6FREROeEbMAOctTFvVLTjOO/CbqDBe4hweAJ5eOfM+hRnGNwN2XZsTom8HctcAj5qZ7uZ51EOicf16GEQksgwDLNCi2ldheOoaoplqofeYFDEm1l3Sm8RpkyBEGeBQGj3uAbMsUag5OGZfnbAeRVAhqtViFwaiH1sHqlHme+hVXQS8vg3Xv/Ezrm1pwZcM9d9LMGXmCSMGEIElmdso6GOMOjSP6lv34GJpzVIqNZRnFK3w4Gc2HAprsBAfPZAoxHWcCZ5iLJieOEO7EfdhCy1BvQEME3bMUc3w0IRORgJ7nkSwFPjWY2HegLh5+HeC9iBO/IjGMzuFqy+NTz8xZle85fefGlY5nIBEdILreHM9iG+aaKbNI6lMcy+z7IX8fhvisgDEfkF59yXnXM/75ybisgt7/2HIiL4efNpKzvnfsY59yXn3Jfqtn/aIqONNtoV24t88RMR+WER+bve+z8jIiv5DsJ67/0Xvfc/4r3/kewF84/RRhvtj9depLj3noi8573/Tfz+D0W/+Pecc3e89x865+6IyP3nbWi57eT/+fqpyCQUo/7F734kIiKvfuIzIiKSptRBM/plC4aUVMRF4cdSb7E9M2ichc+oJzebaUjaC9s3IfyN2VqC2IQ4zlg/OSE1TOCh4lMYQs1qSzVVtPNMFalAIZOIPdJaxSbU5z9TR8QYQ/Wwnc0CbaxHGv5dXobCW4S5hHsbLbhtllD/tYMNKIpSyZcty3f3QnHu7mff1H8gjfj2N749fNZsTnFsPCYtvB0eh0IThTD2DjA9tsfrarTe8BxM57r/rDTtWaIa0VqLhqJciBonTCemeozLtZ5rbdB9RA62KAoWeD4sGSu1//yQahixi4QzA2wVuseWCMVWtvVYxI2MaEeL+X2qMNeNefYwN+BPUfxFmmNBeLOpXr8FCpgXl2xRh2dnr5j80dFre+8/EpF3nXOfwZ9+XES+IiK/KiI/hb/9lIj8ygvtcbTRRvue24sCeP5zEfn7zrlMRL4lIn9L9KXxD5xzPy0i74jIX3veRqJIpJgkUpk2xwavtQaFtjICgaLByhcF22jw9JhR97Vp7Xh6bBaTDOCl320b0cPZaTDHmQF4bGJ7OMkmIrIGnXU+8APo+g9Mce/iEsvMOHsfzmMP7Tu2n7h+a7Tae08FXch9AVP+/lthH2ePdB8ryGWtDTtPhtbecYRWHX6vTHvzHASWvSe5JCKiKixTP/p/9bpgkrC3AiW4NwNHEepU67OwPucP1hfqhT2YYyIzichjO9vT4zk0s+XTA5B8wkM6R6y9YTtCMSzL2T6DdJUROGFUNrAUYXu5lT1DWzZH67UyLEEN/s0Z+8lEo5rGtHlDnY4TkTj3bRAoIR6fwiCtAQCtLvQ+ni+VRDXD1GVvpvMuL7UdeHh0R/eZQzG6Ds9XWU52iqfPshf64nvvf0dEfuQpH/34C+1ltNFG+xNlVyyoIXK57CSahjfZ+YVifs7O9G03h/BD1QQvmBO+ea4ehWwmVrKJABGiZ7su5ObDMnDjQQ7KQGXxkp/mpMCmuIJZnzk+croaEYAFYxQT5ISek2dhxj0GdU8NkBI9bRqbnBTcgaf3NW//5tfe1Q8MxPTskV6zJfLEI8xoi4hcrvVEFhuNEPbmun+KTYiISMJ8VX+t0d47Ny2mHm2sCnWAwqSOexT7ZL6LNlhnhD3pD4lYzh7CcxpQyruxnuMB+BUfvBuimhsva5t3/3UFdiU5QEKlgWI7gL2Q/8doy5lLLin59PAZRUCt2OQgn0ZJLnOtOZSXCIBhyLEn83AcMXayqfW+0Jlneah5bLfq1UvUMWzE0D3Qc+vBvnR4fKDHGoUTIQdDlOrGb9y6reua7TiXDKIzz7OxzD7aaNfQrtTjZ1kir989lvfPgjderhQos4acdHxDxR4KIxKR1pyB3s2NWeUXCYCKMDzRP+UzzrEDDGJqDQwCKhwHsT2miDyIKxJ4soDk0aNHgSsuQ+W+BdQ0MSy7wyw6ooEKrLbLKqx/8UiXeftbb4mIyOo+qtAml3OxegKe6YcfhRyfOWxBYVEISa7NjHkG79Ukeh/WF2TSNVx1ZHyhtFgUOjEJRT/ATdewVmAGT2rUWC5RM5njujjLXIPrmUBQZPUwnEdSP8TJ6mfHr6j3XJmaSYnhIPrn2HFYx3DmwbcNA0jRLgRXRGTb8DxwRU2lnJwBZFQiZHdThfy9Izsxax+kjTBl+S3FV8lEtMPMpNdkhXtF4dPchC437yhMZooILon1erRNqKvkRTqAzJ5no8cfbbRraOMXf7TRrqFdaajvIidJVkiRhGLWR2gB/cEDxf+8+rK2K+I6hELrRotADNGXKJQcGpGHFCEYqY57SwftqJCK6bwtwk1Dn1Sg6EKmLAJ6AuWSSIP0ga1D5gP7s1Do6YEBJxVYYootnsSRyHRIAbZ4Nxzrvfc0hFw/gOY7zqs2LaYYIaVjocoAVtqNbvwSxaxbhbb10gsTRh+D+gtAkTl13A3gpMY4H4//sg/jfSvqArZsC+rfG28IJDHjHqHFdrZW0M8O6Sci5He9hq+XZpZ8+p4+D9HpAxERefXyDRERObgdCma3b+h1Jw2VAPSVmLSGdG2k3iIdVmxEM3KkTjx+pp0iIkms9yZFAS+llp6dK6DKLsN3XNe6DunAIPj4FADQg3Mtaj481wL3SzdQrDVFXxa/vdOQf4Lrm2b7wzJ91w607s+z0eOPNto1tCv1+EmcyPHxkbz/wYPhbz1m01ePIPXUqRffLwPQ45z6GXCVB2CVsa+tDkWYDWC4lg6aLDiEQk4AHPEGslvVBAcBKMIJQLNMhjbi+bmCKVaYUV+YZQStsZnXfXRrA9vEpNzpua731tsfiojIo4fBe9xHobAH845He2/uQ7GTIKOLHu0j08HpEJVkW7SNINqRGLrxOxcKlEnmuk1CoosyeKiyRPEIxajGFFIfImJZLXT5Ke7V6cJwIMDrRvBMHgFLZmitDzHd+HCL62mYiGaXur+zSL3h2+d6rj/8g28Oy2T9LREROb6lU4W8Z5Vp5U4gHkKA1mKl1346Dew2KzAi5TNGfeGeNSgIO0KnMWjmrZoyp/GwXz4zUWa2AyBWgxZsZjgY7j/QQuZHD/QcXaLX/mAa2rTNLb1Xbz38QD9zCnWf74Xn/O6btx8bWf14Gz3+aKNdQ7vyHD/LUnnpzo3hb+cf6Zvr62+9IyIiC0A/7Czzxbl6wRlpmNFOq1ID38TPJFNP23ojighUD3Pz1uu2OeutB6fLVBi0YM5fFMEzdGgTMd8j20m/Cfsq0IKpKZywDu/Wi6Uu9/59zde+8b56sw/eC/nz6VL/var1nA9u6Ez2G5NwzUhFPrTYLGMMPrsAT+HZPXgRMxRS31HPnODYSrT38qXxuIgGOErdGQHIC1Bec6Z9uUab1IWaywLAnwlahDHua2VaXO8u2cZUj1s2Yf0/xLDW6bfR1vN6raxYRoL95xO9R8VcI48yMbUfPDOc75/PEe0Zl5dioKgFl+AkD8NKMXJ8j+tXoa3XWZktBDEewCZyJ3SW1w+5fZzq8Z9ehHv+ENevBZDp62+/LyIid18Px/gyWp5v3HxVz7lY4fzC9ZjMZi8M2R09/mijXUO7Uo/fd70sLrcipuIeo6L6EHlO3ejb/3gSm2U053GDDDPy1jS87Tg+WiHvzI6MHBS8eNftDmzY4RgW3wnSYS7YGy9G2OcCwoU1R27NiOfycoXP0EG4DFHJhw/U09/DSOUF8v+FC7m1O9L9Hzj1OlMwrVwasYubh+r9c8BzkyRERwczrfp28HDnNxCxhNVlud4VdVwhIqqrUPlvwQrD6MZyD76+B/goigsVSi5pHvzI0bF6pgqjsgTXZEUAApFVZwVvuticD58dw6NuDo5wPnpdjvLwXDx8pNHiARhn93HN9mdhHwRmpRi2GkRZjMtjlESxysgIWfS47qwv1ajgt4bJNyH3INiWkqFW8aRAyPkS0OEsdILeeqA1jodn2vn4ge//IREReQ3sVCIiN4/0Ik8gf350R6HMRR6q+jdO7uywGD/LRo8/2mjX0MYv/mijXUO70lA/imOZ7s3kch1C2yXIMT8617+dY248NphsXyPsRKgdMzRbhnBrf6ahEPH467VlWMEcPABAPULLNDVMKzHm8NH+6cHuk04NRTIIK6kPz2G2yBlwDDDhi3P922oRcPgkgbw11RVPXtff33zlIOwDwCV0iORoT8PW1AwbJqBvbm/pOUcS0pH5VEPIKdp43Wv6u5Hwk5badwA7bQAO2rbhPGaYWCs5iZeHtOiQWnkoVi4xs28JKDdokc0Rdndof3kD8vFIh3Ks70wL12Hycf79r+n57+t5nMyNwAmANlsAZfyZ7mOWhzSvAO49i/UZyBP9GRlg1cCzAKLU2ACAHM6/EzyDGXUDjTCI323RObRiSa4qIrJCQbTY0zTtg3eCyu3b7+kEZoEw/RaYjO4cB36CT37mZT3GCVikAJ7KC5PuvthgnoiMHn+00a6lXS2AJ4nk+Hgii014S50V6u1OTxXMsgaY5CRgFwbgjQNF8Rbts8IISUhEoAXaga3RYQeTyR5opHvHNpRpw8FLUEmWc85R9+S7sUex0KFiFreGMrpS75mjGNWW4RJPUMh8ZR+Kvgl4BupwHBScbTD5t4Gklp0kFLRwXEXqbcunh2NCOHICYRBv2FwmM/UkHafQWNwzBTxCjgtO2bVGfbgnGEVbhfOKBcCwj7LU/Ua4VzGihEsDVo3I6gNevaoP5zg91nlzRhf7J1rEOgjBjSzB2EPOwXXFmXsLmsK2ER2QAzE1gKYaU4UJYNql4f5bApREXgJOK7beFPcAB86G6VFsz4ftpGjxtaAE/70v/8Hw2fsfqff/s1/4PhERmaAlvV6G83gPDEyHR6BEn0PwxUB0E5e+KOXe6PFHG+062tXm+JGXycRLZ3TUVyvNzzZbfRN/+5vfFBGRTx1/alimHZhhKMvEAZrweqvhaSkDlRr2koFjD8M5zNHjJJw+WXoTQB4J63VmcIStvtl0gu2qF3Fb4+kAWLkEgGg+PR4+I1NqiVwuAihjsQ259SICDBeSUfcvddnlysiF4bQdhlJWFyZygVjII5x+vNRtZ7XZB3LzKaKB3EG806BaHt7XIZkC7LjVwwA4WYJ/jmIOUY8WqmELrnD+E7TvKGYSmTn2rIBYBe7DJAlgqZMTcAbOdR9FAajsxHpRvf5HJxiM2iDaKUNuXU64vh4HEa3eRh6Izjp4cYqCioj05BICZNoDEu2MkAUjjKpj5IAhJrOdCMe2BNvul77yteGzGICh/ZsH+KnH+vJrIexdrbVWtFjr80HuvtiwP924dXvg9nuejR5/tNGuoV0tZNdFUqS5xAbAMwOjSATPenqq+U7cfz6sB7hoR7lsDMusDZ98g8GKFAy49swGSGVBxlhCTo0MEnK4iGCfKQUxDVsv5LlZmSaopTb5M2dUJ/BMU9MVSDGHmkbUBQC7qylVUHNksdJzfilFLpcEj70CZ98GtYLIQJeXldYE5jkht4iETHSTIGSoL9SLbMDh5yxn/aHuNwb3YWJYdtfoxESIkpxnjm9YjyLmu/q7pwx6HpL0cg6oLa6VcV6yd6TV65Ly4IhgUsMd2Fa6fzLokAGnbUP+XSHS4b1KMRhkx1crdDN4iSpTc+GAF9lsh8EuwwLd4nrkgAevV3geGpNwI5rqUCO4fx5qJgMvIwBNJaK9yHSdXn5VoduMXNiU6IzvjrPIkgc900aPP9po19DGL/5oo11Du9rinogUcSSrdQhbq1ZD+xITVt98X/HKlcHIrwGemCCk6nuKLIRlGoR3KcQV6s6QEAK0wWZVlgAUY0LLBC2tFXDsNVqIUwMGiVhURAFlwyk1y+SDjWYo4JUWYEESRgCISL5ZFCb8FRS4oBXf5dCON/PbRxDEWJ7pdaxMa8rh/LeRfpYiHE9NaBuBwJLKr46fmeLceoFQdIEw2MaQAFcx2qXyq/UiEbbd4DqSXLLcD3nN8UvaomOIn5jQtpgDoDLD9aN6bmaKamiPRixyAjTUNCFU73vIdKGwOoiBGAadjPr2+JtVH3aYVYiA1WfqZ7kc9qZ6Hl1L3gfdzrnhBdiASPPBmV7XL3zOpLJTPccZnqMNyE97U2xdAKzGZ748QptzFiYJvYskfkF9ytHjjzbaNbQr9fgiSlPMgp6ISHKq754Hp5ARWigog9TTIiLlPqCpa3pKSCdlBuLpKFGElo6Bj5KjrmWhB3hYX4UiEOe0i4GyGYXAPhwH98Hi0ZZtOOM9IsxvR2Rosa9Wz7YRwEaYJ99UwTO4QbxTt1liEm5p5r8jtOxmpV7HufEM25V6lP6BXocSy9jDcBnPjeeFFmgZ7ssERTACmdh2FQlAlcW53rMWxzwx6w8ipBmiJLLUxKEAODuGq3ekPTd8fFO0xBLSWvOzsP7Ar4hgZLnWKM/OpAfREHhsQHitaGaNqTxCbG1LjCKZggJmgkKzeSxEALRpAdzpyZdgokUyMf3fX/8tERH50R/59PDZHJyBKSjlC7T1CsMrcOcVZRtyAH2FNl74Dmgre+TcG2200T7GrridF0tZ7sndN14b/vatt5V5h6IMH5xqO+q908DLd/dABxTIYtoQPml49dLHcvT5QZhTdgOjKhlXmZyGt+MCAIkcb9kcuZ3VJ6CYQo/35WyGZbMQXWQJPQM81k5ujY3Be3TwFJmJTpYrylrDCyNnT9qwDPP1GqCezsiNlZg7r+CxC+S2znhBMtUM0lE41qYOUUWHtmaMGfu9SRgY8RiW2puCEZlePTEsveDoyyi6iWXiifFQbG8ignBWyAK3qEVYQi/amxYsGXBjPDvlVPPdzkRgNTx71FJmi7x6plYAeDevIZRVbAAAHWVJREFUVWcELStGYzGl1fXXzEqj8X76bmed3rT8zh9pdPTWt9/CeQ0fyd/6m/+RXge0bNNCt/c0XsAMEekEA2eNYVbK0mJ3GusZNnr80Ua7hjZ+8Ucb7RralYb6cRTLdHIsq+Xbw9+yREPycqZh5gVm9b/x4K1hmcNXQTskGgo5hFC1QVhlIMuM0M44vQg0TkdHinnuiaVGNWi5vBiWISlj3yCMBrZ6Pg0CDg7knB6z+h4hoYk+h/13iFXtVNwQ+gFpFx882QaLEe5WmFIUnOP/3961xViWleVv7bP3Pre69236MtAzZIQZSBCcEEAfDGAUYsQHHjDGEIPxxQQkJgbiAzHxhYQIPhgTIjHGGDGORMgQb0GeBwdBRYYRnHGame6evlR13c517718WN931l/d1dOFDKeqU+tPOtV1ap+z11nn7P3fvu/7F0zY58nZbxHVNjHhnmPYu5BRVppsNDsBdiRxTLYMFVIuGNmmms1Psfy8gdXt1JTTyjUrPnx2tZ1ZRx5CzeEpbTLeCsN8y2adThU0zbRePqYWaqY0wKQDY6YmCsw9UZOFM1z5WWsu/NzY3OW6TOpDrv6IRT4rM6YisVB5kjb3Zj/HNZl/ao8yrdo2cuOXLl/h+UNKWV6Lk4G//PdfAQC87a2vBwC88bFQ+NPk6PCa4peE/WvrszKhvXP5nnTplSx5/GTJjqHNfVruQ69Zw+UXImPtafdcWAhBDwPexa/fiN7j+pVw53xNjwKSdcDon1iOBScNTGiXHKjRMeicOtxlVYQpyEZb6kVvPiY4qE8whsYwZYadN+H/yxYBQC3xsO1dNngWqf4MdyOrLU7tZcFKHsp42i6x2C1GEyPi8r0hWu/WFMukNxybqEJFQVAHoOZ+Tpt4zA6LpDnFRqdsa44H0UPJe+q8zrTqarbalvqKOMLz++34derTs1eCoXOPnJkeLMlp8fidiZxcIeUbttg0WdfoEpRUzJmyt1ZyreNxLM51iHvvdUNUU7HwNjKj0XIW6opanPvoD1XoG3NsW8mopGPQX5u7IXLM6I1VbK3MmK5bUvmhQtIbz5+Z/e3cqXA9XL0awGsnTwZmZHc1Rnltnm+i4TB8r1Yivqqme74nr2TJ4ydLdgxtzgo8LZw4sYRz5yLP+M1veAMA4Fv//iwAYHs7ePMr6zEH2hkFj12cCMCGjEorg10z3JDAjs5KuONtTmKOnzNPLafBC8sr2uc3bJe16WHEausYEIZgvBOeq8eoYjyO3jTmh/RYJgdraXinBPXomIZmIEeL3ka5Wq+7l0cORC8W79vx/i2JaM/opGorKojPnxI+OiTLcHukqGAfzr/APUYrr8vBDwOqAy33xYiMUOwhOfGqnUjjrq5jJNfwsZLRQW3eZOa0FmkwhJ9DM0y1yxaboM+UIsTItONA1aaJohknKXGr/cf/OEHCzegrtjUX2IaUR900GgpZpfFc4fs0ZF3h5etxP3cZlT362osAgAuPvW72t3Pngw7fmbMhAh1SLejqD16cHbNC/b2l5RCRFoXy/7iOoiwTOy9ZsmR3twN5fOfcxwD8BsLt9z8B/DqAswC+AGANwL8B+DXvjRDZvq/jURY1Ll6M46DWqUJ77XK4g718PZB2nr9kKqLnwl3tIodL9MjJrieRj9/vc3RVxXFbJn/X+KMOCTPy4qrOB1M1nsAVwlpHjRl1nInHz7yX+VVpSDIapuAykioMn19ElUyPzfjsxmMzJ9eNW95H46oAYKzxS3yeMw6ux/exzoGet2bjriJZSNHAgLn5hO95PDAAHnqoRaoXN1XcKz/0fN/hmE12QtoGiATmuSVBLdK6y4r4XrOZigxVeg3YSaWAFtWSRJapTM1FAyxa/FuHOXV3xQgccI9ykpwG3I92Yz4XqumU+Z3DKARPliKzPsOWWYe0A0vWhbY51OWl61FJt2aU9/hPvRkAcOJMrE81vGy6HNd29txZPm60AwkXH7DWkLEj0zaj4hsfFYLuZff0+M658wA+AuBx7/2bEGLYDwL4FIDPeO8fAbAB4MMHOmOyZMkO3Q4a6ucAui7MPu4BuALgXQCe4N//HMAvv/rLS5Ys2Y/D7hnqe+9fcs59GsAlAEMA/wTgGwBu+UhKfhHA+Xu9Vitz6HdzPHghtvPGkxDS37gZfr5wJcz/funG1dkx1zZCyLQ5CXPhFimDlBvBxOEohFcNpa6HQ4thJouLKcIGWWVNbWW1GL5D8k31nt8BoGAYr7RCskdTU3BS+0dtFmcKTZlCeoE/+DQrAZaJE8+oM2frsZ7Ee/TiCYa/LDRtbUYgUtkJKdMycfjtzRDaTqoYmjZkyGWsLg6pb1AYPn6LWP8eC4l2cEN7IZx/kYW7MYtrC/14TMkZdyp8aXadM63PHaYaEUsT97rONLiCnyfbYbnByDti2lUknAy2+XoGhy8NBPYVhypkmlDdc7N3yX3IjUyZ2rJjrlUfnS1EjpnGDJhSCoR2bSsWmHvcm4sPBwmtM2dPz/42YsrimCqozbm0EtMBtPbO49N7tNJwWfYqAnicc6sA3g/gIQDnAPQBvHefQ/dtIDrnftM597Rz7mmhppIlS3a4dpDi3nsAPO+9vw4AzrkvAngngBXnXE6vfwHA5f2e7L3/HIDPAcCjP3HOT/02tnfiDaAkUGNlNdx1T54MBY4NI0b4wkthXvigeSMAYORDsa9tRsA+UFKOmVDTdjfe+WoNNqAXrniHtewpddi6BHrUucA15o7K1pDYeIKDutIMNaC3EGAmNwxCtYIEWClmBTt7l1bbixGHtAMMMKOhdLV6fFbfoBkR/sq2VePCe+0YQBO3bwYDVXQzcdGLtVkM69KrWnWcLluEjq2502sre9YKADlfq6XhHyyI5gaW2yMTUu+t241FuYyDNAQO6rFY68waxdzr8NiiFT67idE3kHbBjH8vJR8zaVnTlEtGJQsLESq7u8vvKpe9ta3imhH9ZEF2i8pSA57/6vqN2TGPrL2W5yCkejG+13ZHGg6UK2eRtDDRTaWiohNcnGs35fQMHgdU1z5Qjn8JwNudcz0X4oh3A/gOgK8B+ACP+RCALx3slMmSJTtsO0iO/5Rz7gmEll0F4JsIHvwrAL7gnPsDPvb5e71WlrXQ6/excXPHPBa8x9py8NjiGa8bhZTNDc4PXw93VCEZF3rxzry9ycERfP7uyNzTOIxhvEVopZRvRkbVphLxp5qtFQC6PcO7FszT847Mm/7YrLWgp1RuWBtQjLxdNusa8S5uYKgFPXXp95JTpia68YxgSuZ3hWlDjUT0ZhQyXWSrbhQjqE5XeWv4fZWjxRZ60QtW9OZtfj4d2zbiHq2tBk+fE8JcmTFbs1oJ58tLp8CCY/rco0p1EAOqaRgF9HjMLtdfGliwWn2TgYZnErxkkk7VZSbMw3cYbZa5lcemehOf2MnjPhS8RASyytkGzJvo8W9Sy6FmWHBrI5zjvBmGKqjvc88GUI7Vi1hbCzWvmZ5fHY4dbJkoS7oKkkfkftr6EJzVnnhlO1Af33v/SQCfvO3h5wC87UBnSZYs2ZGyOSvwOBRlid5CBNe02+HOt3Er3FHf9EjIhcaT78+OabGU+vKNMPjh9NKDAKLmGwA8eDrAgCdZeB0LbEAT7sCOems5aZjDqfGi9ERidE7oPXIjlaJBB5BmHiutpal4q6qa8W/jqVUUVp5LwAnv8Lbi7ulRWtL88yL0xKiiqVmFJnU4c6aDQSKSUD0lPX9WxP3YXg/VZsfIKXOkqhqQjnA2RUmtusIqzhCiSgJRj1ukKMdaQ7XfKdffMaSSURWq371eWJsduukYFk0JLio6AlTFKM0RZq18vcuahY0qmlrKyAJWsSNiRmAp6mzRW25txe9VR90NkrdGkwAlrwwtVzJNikBWT4RjF0/EiPTkuVDNv7Edos6XLj83+9ukChHwmdMBuCNgWWG8ubpfjpdsLi1CU9XPjRLUvSxBdpMlO4aWLvxkyY6hzTXUb7zHeDpBu4wh4QbDqrOnTwIAHnvkIgBgaGD/1y5v8NjwczQMIZEbxxB3eysUeOoOAQ6bMWxcW6DcMYErnoWWbhFTjmoawq2GNDbHoQzTzGDDOyHcUlFvMtEE1Xguce5ztq8Kg7GXLLcjfzxvhfN701asJTXN00oBJzPFpBF7OQvEpHvTw2EEiHqX8+252PHEFCCLEIpWXHebIJlqFNOBXRbMMnIOGqN2dOJEKEZppiH4/CYzPHiGyGo5SkByT3uTfIABJ+ja9+EZkivlkoKPbXxGJqTkwqUhEI9RqK9iY060kAXp1Cz8aXqyN6nCTX3n1gNXXryMwU7kcEgAVC3YM2cDF8WZ0HuZqejFlSA0a9vEWveAwJ+1tfD8zGgXqB1atvUe+V0ye+5cibvAae6w5PGTJTuGNveBGh5AGYVF8EAntDzkUKZNQP5m7XjX/Wb1DABgMAjQ3Xoc1EsKc7fbpTJJNw8wR59FDzeqBDENHrqh5LP3BirLAl2XBUDfhN9LUxRbXMi41nBn7vD17N1bYBQp2FRG+joqu7DwNyEM1YBB8kJeR8VBFtdMm6Zii1BO3EosSz66xUKbJKw7hhVXsACo9pe0CFu9uI4ui6OTgablxufvstC0xEEnExbcnGkrVmQyypvJO+fteEzOYulU0F2zD0UZopmylnS1ADjR54tt2WJRbzrR98EUSxmN5Iz2ZipIZhBF7cPnUDHacga6XJIRukXZ9+EwRIa1mahRTcLxi5zw2+ZIru6DUWWnILgpU3vTRJuagOtZcNRH3TasT8lr56Wg5ZwgXVvETokDCvAkj58s2XG0+bbzsgztsofxMOZHukPpLnf6dMgftzYi8eTUqZD/X3ohKJUqkd3eo2fH4YGcD1+Yt9Zh7rbD9lNJTz0ex1YbCg1yJzBCAx3GW/Ecebijt2Ze5044rfK8hn/LTbulqsWNpxerdfc3HwPbcCKjNISomjIAFpbpzblphWktzQRu6CErerO2URISYCVr7eW6W4JHDgKROLveDqJU284xCnAiTRkCjkobbbbvcuobWICJYKizjqlxXvVm8KyeH4Qg0Fkn7pW8nuC4wyH5/eYcgr9KCGnI9upkGkFkNbUKrrPNuWXUcVek0Nxo+Eg4dme6MTtmsR+i1jE198aMKPtV/NCWT4TvsFq/duiHdAVLtiwrqQ6bqEKt5Jzvp8UN9vYSdkVS2U2WLNndLV34yZIdQ5tvcc8DftpCnsWwUzFsNaVQIaWFKtN+unCWVH8imlrk1S+XJ2fHDHfC84XwmtbxntZ4iVyGxzYpS1WadsvKQijiSH5J+PnMiBkKUt4qVFgJD9iCisQZPZl7K8sRr10ZGWwAcMSLj6YG485Ird8O6xlxTn1uUFxeYaJo/Y3tXzFcZJipllBmEG/TSnwEIt34NZiaPe8xxB+6kOrkZi79VK3WRoM57kQpivnoOENPs967hhU34yaWKqiaVINF1oztyLZTy82kNcwNppX+Fo614a6KeSO9NfIcqqmZjycmpQt/W102zD2mh+MBEYSU2VowbMeLayHNHDEtyxnOZ0aKTMVAITELM+lZKV/D9Ew7XZnCXU4egNfsPw06MelAgywV95IlS3Z3m6vH9w1QTRrYdovucpqn7ui9asN4E/hi/UaYoPvo6wJWv7Gy1gSTbG0RZGPacM5zGMIovN2uJtmaoto6uQKdUsKc9JTmFroo9hrbiNNMYB0jC51rcAQlrEexONhvL/I1WZTiS+dF9AyaADwmj75c0pgtU7QRHl8kcSPWWQuAxMm1Nb2hnQ5bdtU+onIN23Mdo6AzIdOuq7aaGSAxmwY71WfFApxpKyp00Tz5KQuA/V58HRX61E60g0WGQ0YPajVqj4wXFShJ+9iQ1Wax+hK5rBgJ1RI4Ne08P+FoNkZwtkXW7nKABqOj9VuhALi4FHH4Y3EeWNzr8m+F4fWXZG3ms5Zn9NSt1t6CnKKB0uyHImONS8v5mReGSZi38iSvnSxZsrvbnNt5QKuDPW2bUkw5JmGLq8ErnjgVAQ7rV0Pr5DWnOd6KUNPlk9Gr39oJx0zHBEEYbz6lZyqk30awT6+JtYZ2HjzbUj/8dARa2Nbj9hahstScy9uaOmE8tuCvBJO0DcddgzQq5ugtgjFsblztqrVDhRbmcJmd5y5m2Sy/i+fXQI7YfiPoyOTP4nBLK06eqmjH/ZSDrzhmy6CjZ3oAGcRKk3c3CSaXVFeK6Ph7bTw2h1IUZP5tb8dax+JKyJsrgYO4r95EPlIQ8owKut29ikLh/6y5UJnJU71ISjjh/Bwwuhs+651BjCS1Roz5Hlnf6fWMWhAjsNyTVcf3mJvoRAAm6RO2O+Y7w7dUsoU7HmrganwfGkwidl4tRqeBORcGtHYvSx4/WbJjaPPN8X2DyXg4U7ABomcSeUJqqpmp3vYWwt11SnTKDokTJxeix+7S043odMZmLFWXOdPumJxmnn50K47pOs1cev1GuMNr6GZm0qwpk0nOmJyNR26bXEygEoFILG27oWZfJuUd5nZdw2NXBJRRLXjEcUp2dHOH3kaRjB3h1UAAD43e8lyH8TAEf7TJg285AYruLAmX/Bxs3iwdujHrFxqzrTwWiJ5N4JqSUN2bNyPwRfzxa9c2uGaTd6uLoR/8fkyNN28YOUmXb6r9NNp9s5Hq9OYjahjYro8DB2lOWQcw5Jhddi4qfr86XSktG3JMHl67LkNO3/Sl8xc7KQJklQRS2f1ssZsgReWiuxegBUTikPZIBClFQoC6PYmkkyxZsrtYuvCTJTuGNnfprU6njZaLIfq0IqCCXPWlJRbwTNi3XYR23MWHLwIAxhxU0DVssHPkiF8li2rHTtIlYGjCsG9EwMSKCU0LYfUF2VdhpRMLb2pJERKOxSWG6KbwpoKOwthxZabDMjQtW5wOS1ZXMzY8dIb9eSMGIYEsLn5Uuzth/ZrDbsPfNiWrJ6zGCZTTWojvFY1mq4fXbvf5N5OWDFnU3KrDz5VWTEcyCl72lU7MsPJWl4CsOKfhHQT0ZDbNC39rE4e+vWtk1wng73eYjvDtDyb7yKVpCjFbXDvb8TMbMS8bDTUFmSnUyKQMbGdOyATsr8ZUwVOQVZOVzy1yGMwgPn/EKcE9gq70dchNCqhWZ0aZ8gxxPxu2VcdjpiVspdrUSWF/nkvwlY+byL7O/B7eyCtZ8vjJkh1Dm3Nxz2Mymu4pbEDsM0F32aKqDICn3Q0eu8U76JgTT2tzd+sQqltodKxBMry8G4pQiz1xzIMXmyzGO/IOh2x0WYTyLMRNd4zYJlt9QxWTdim2WUQPs9ANbchlhGM7XTPIgu/NVRLJ5PMNCCOjYsxE/HW2GfWew/OCh9BeFUZNZkDlHHHU1fK6tRGZjF2O2VpcDGsT8MUZtSFpFeQseNn9lLz2mPBkCYtm5vnbN29psQCAPsEso8YIanLdJT3+xEQuA7IsWyUjBilwj2MEJZCPCp/ZTInHoljcnmPqRhFZ/FyGFC8daezajonyuH+tBTIpV/rmVYMphikZ5fT4HRqZdlyei+UY1t/v7QXeAPEzU43VmUhSLdiq4jAZFp9bprjXHBC8AySPnyzZsbQ55/gtFO0FNPWdfHxx5EVGcXaoYTccdPpUUNcp6DVGxkMoN5dXd0WU+bl6PXifTeZ+a0uB3FMZvvRNAnY6zCEfWA087Okk5p2SXS5FsCC4xNtlSOq6xXaWIfkUgiW3JJkdHjcK3hDqdfMWVX46AvBED6W8vWEdYMeMJKsaPo9tI4FjvJGT3uYYKLXYWj7smQZtANGzag78lvG0I+a7NaO1CeWT+v0IAPI8r/jvjaTMTYtK8+QFJGoZWLA8ckV57Zo1C2eGjygC04c/2CGhZhL3PJfHJvBmeTFEZNdvRr0HLIZoRDLl26MYHeWMfE6dCu8tq+78PHR+xyhV6kvy8uE9arQbx7gZ0pS0AhTBOWjwirk8vdR5yOdnJJY500p2zaydfC9LHj9ZsmNoc9bc8/CYzMZUAZFCWdcE8mQakhDvZCKFrCyzCo1Add26ET2dBgtosGW5B7apijK15gh3XF2NlNnJZiAAaYDixHO8U8vQNwkCAUcug+CWzQ0zJusB5mI9eVqzDrr4ZrY2jssylNeKgKaiK28oBR3zURHM4pjUdU3F3tMDqApcMZyYjOM6eqriQ1RRRlDmGDlmjQLfNUrCBb1WQ09dEeSyfitGct3l4EVzqu2OCS3NKgsxJVybHYSyY4lAHHlFIFY1lR6e6YAwGa75t5rvw1tlZJLANBJ9eCN4+noQPa7ISYL1WsjvIv/WU05N9SdXxHVoZHa7pdehqrMZ8LlA4o6fjbc2ZCXWbATLznlZeh/fhyDgBdcROwbWwx+Qk4vk8ZMlO5aWLvxkyY6hzT3UBxr0+5F5NxwO9z2yZZhNEneU8OG0Ewp3eXNldsyIxaslzjjb2Y7pxPIohEXi6o9YcKp95EuDYJIxC3CXKcZ4IreMNc1dI9CCIeXAkKJubofWoVpUGoAAAF4px4ShOh8fj2JIOGW7RkWpiuea1jGdUJ1M4d+i2U+11DYIZFIBcWMz6gIMx0xDmGo4H/al140TXKVANCBgpDaMtclYijUtrid8jVZPRkWkMU885V5PfQjnrQT3znaYH39yNRRtGwPO2SRwZiJ1Ie6RR0zvFOJL5rwSxt6E+kOmiRlTLn0um4bVVt8KrylafGan5ZYCzhDHXwkEZgBiPP+ELzAbomKKvhWLky2y7JReAaEoBwAZw39lGnYSrj5Htb01S9G2xsvy4Jdz8vjJkh1DcweF+L0qJ3PuOoBdADfmdtJXx07i/lszcH+uO635R7PXeu9P3euguV74AOCce9p7//hcT/oj2v24ZuD+XHda83wshfrJkh1DSxd+smTH0A7jwv/cIZzzR7X7cc3A/bnutOY52Nxz/GTJkh2+pVA/WbJjaHO78J1zv+Cce9Y5933n3Mfndd4f1pxzDzrnvuace8Y591/OuY/y8TXn3D87577Hn6uHvdbbzTnXcs590zn3JH9/yDn3FNf8187toZQdujnnVpxzTzjnvsv9fsd9ss8f43fj2865v3LOdY76Xt9uc7nwXWAh/DGA9wJ4DMCvOOcem8e5/x9WAfgd7/2jAN4O4Le41o8D+Kr3/hEAX+XvR80+CuAZ8/unAHyGa94A8OFDWdXd7Y8A/IP3/g0A3oyw9iO9z8658wA+AuBx7/2bEJhWH8TR3+u95r3/sf8D8A4A/2h+/wSAT8zj3K/C2r8E4OcAPAvgLB87C+DZw17bbeu8gHChvAvAkwiI4BsA8v0+g8P+B2AJwPNgnck8ftT3+TyAHwBYQ4C8Pwng54/yXu/3b16hvjZL9iIfO9LmnLsI4C0AngJwxnt/BQD48/ThrWxf+yyA30WcU3QCwC3vvZgER23PHwZwHcCfMT35U+dcH0d8n733LwH4NIBLAK4A2ATwDRztvb7D5nXh7ycLcqTbCc65BQB/C+C3vfdb9zr+MM0594sArnnvv2Ef3ufQo7TnOYC3AvgT7/1bEKDcRyqs389Yc3g/gIcAnAPQR0hhb7ejtNd32Lwu/BcBPGh+vwDg8pzO/UObc65AuOj/0nv/RT78snPuLP9+FsC1w1rfPvbTAH7JOfe/AL6AEO5/FsCKczNd7qO25y8CeNF7/xR/fwLhRnCU9xkA3gPgee/9de/9FMAXAbwTR3uv77B5Xfj/CuARVj5LhGLIl+d07h/KXJAE+jyAZ7z3f2j+9GUAH+L/P4SQ+x8J895/wnt/wXt/EWFv/8V7/6sAvgbgAzzsqK35KoAfOOdez4feDeA7OML7TLsE4O3OuR6/K1r3kd3rfW2ORZH3AfhvAP8D4PcOu7jxCuv8GYQw7T8AfIv/3oeQM38VwPf4c+2w13qX9f8sgCf5/4cBfB3A9wH8DYD2Ya/vtrX+JICnudd/B2D1fthnAL8P4LsAvg3gLwC0j/pe3/4vIfeSJTuGlpB7yZIdQ0sXfrJkx9DShZ8s2TG0dOEnS3YMLV34yZIdQ0sXfrJkx9DShZ8s2TG0dOEnS3YM7f8A1NOCdlvaaiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#y显示原图\n",
    "image1=r\"D:\\docker\\mnt\\openface\\data\\mydataset\\aligned_img\\trevor\\trevor4.png\"\n",
    "im = mpimg.imread(image1)  ##文件存在的路径\n",
    "plt.imshow(im)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
